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## Chapter 7. Examples

## 7.1. Getting the Examples

Make sure the Drools Eclipse plugin is installed, which needs the Graphical Editing Framework (GEF) dependency installed first. Then download and extract the drools-examples zip file, which includes an already created Eclipse project. Import that project into a new Eclipse workspace. The rules all have example classes that execute the rules. If you want to try the examples in another project (or another IDE) then you will need to set up the dependencies by hand, of course. Many, but not all of the examples are documented below, enjoy!

Some examples require Java 1.6 to run.

## 7.2. Hello World

    Name: Hello World
    Main class: org.drools.examples.helloworld.HelloWorldExample
    Module: drools-examples
    Type: Java application
    Rules file: HelloWorld.drl
    Objective: demonstrate basic rules in use

The "Hello World" example shows a simple example of rules usage, and both the MVEL and Java dialects.

This example demonstrates how to build Knowledge Bases and Sessions. Also, audit logging and debug outputs are shown, which is omitted from other examples as it's all very similar. A `KnowledgeBuilder` is used to turn a DRL source file into `Package` objects which the Knowledge Base can consume. The add method takes a `Resource` interface and a Resource Type as parameters. The `Resource` can be used to retrieve a DRL source file from various locations; in this case the DRL file is being retrieved from the classpath using a `ResourceFactory`, but it could come from a disk file or a URL. Here, we only add a single DRL source file, but multiple DRL files can be added. Also, DRL files with different namespaces can be added, where the Knowledge Builder creates a package for each namespace. Multiple packages of different namespaces can be added to the same Knowledge Base. When all the DRL files have been added, we should check the builder for errors. While the Knowledge Base will validate the package, it will only have access to the error information as a String, so if you wish to debug the error information you should do it on the `KnowledgeBuilder` instance. Once you know the builder is error free, get the `Package` collection, instantiate a `KnowledgeBase` from the `KnowledgeBaseFactory` and add the package collection.

Example 7.1. HelloWorld: Creating the KnowledgeBase and Session

    final KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    // this will parse and compile in one step

    kbuilder.add(ResourceFactory.newClassPathResource("HelloWorld.drl",

            HelloWorldExample.class), ResourceType.DRL);

    // Check the builder for errors

    if (kbuilder.hasErrors()) {

        System.out.println(kbuilder.getErrors().toString());

        throw new RuntimeException("Unable to compile \"HelloWorld.drl\".");

    }

    // get the compiled packages (which are serializable)

    final Collection<KnowledgePackage> pkgs = kbuilder.getKnowledgePackages();

    // add the packages to a KnowledgeBase (deploy the knowledge packages).

    final KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();

    kbase.addKnowledgePackages(pkgs);

    final StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

Drools has an event model that exposes much of what's happening internally. Two default debug listeners are supplied, `DebugAgendaEventListener` and `DebugWorkingMemoryEventListener` which print out debug event information to the `System.err` stream displayed in the Console window. Adding listeners to a Session is trivial, as shown below. The `KnowledgeRuntimeLogger` provides execution auditing, the result of which can be viewed in a graphical viewer. The logger is actually a specialised implementation built on the Agenda and Working Memory listeners. When the engine has finished executing, `logger.close()` must be called.

Most of the examples use the Audit logging features of Drools to record execution flow for later inspection.

Example 7.2. HelloWorld: Event logging and Auditing

    // setup the debug listeners

    ksession.addEventListener( new DebugAgendaEventListener() );

    ksession.addEventListener( new DebugWorkingMemoryEventListener() );

    // setup the audit logging

    KnowledgeRuntimeLogger logger =

      KnowledgeRuntimeLoggerFactory.newFileLogger(ksession, "log/helloworld");

The single class used in this example is very simple. It has two fields: the message, which is a String and the status which can be one of the two integers `HELLO` or `GOODBYE`.

Example 7.3. HelloWorld example: Message Class

    public static class Message {

        public static final int HELLO   = 0;

        public static final int GOODBYE = 1;

        private String          message;

        private int             status;

        ...

    }

A single `Message` object is created with the message text "Hello World" and the status `HELLO` and then inserted into the engine, at which point `fireAllRules()` is executed. Remember that all the network evaluation is done during the insert time, so that by the time the program execution reaches the `fireAllRules()` method call the engine already knows which rules are fully matches and able to fire.

Example 7.4. HelloWorld: Execution

    final Message message = new Message();

    message.setMessage("Hello World");

    message.setStatus(Message.HELLO);

    ksession.insert(message);

    ksession.fireAllRules();

    logger.close();

    ksession.dispose();

To execute the example as a Java application:

1. Open the class org.drools.examples.helloworld.HelloWorldExample in your Eclipse IDE

2. Right-click the class and select "Run as..." and then "Java application"

If we put a breakpoint on the `fireAllRules()` method and select the `ksession` variable, we can see that the "Hello World" rule is already activated and on the Agenda, confirming that all the pattern matching work was already done during the insert.

Figure 7.1. Hello World: fireAllRules Agenda View

The may application print outs go to to System.out while the debug listener print outs go to System.err.

Example 7.5. HelloWorld: System.out in the Console window

    Hello World
    Goodbye cruel world

Example 7.6. HelloWorld: System.err in the Console window

    ==>[ActivationCreated(0): rule=Hello World;
                       tuple=[fid:1:1:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
    [ObjectInserted: handle=[fid:1:1:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96];
                     object=org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]
    [BeforeActivationFired: rule=Hello World;
                       tuple=[fid:1:1:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
    ==>[ActivationCreated(4): rule=Good Bye;
                       tuple=[fid:1:2:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
    [ObjectUpdated: handle=[fid:1:2:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96];
                    old_object=org.drools.examples.helloworld.HelloWorldExample$Message@17cec96;
                    new_object=org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]
    [AfterActivationFired(0): rule=Hello World]
    [BeforeActivationFired: rule=Good Bye;
                       tuple=[fid:1:2:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
    [AfterActivationFired(4): rule=Good Bye]

The LHS (after `when`) section of the rule states that it will be activated for each `Message` object inserted into the Working Memory whose status is `Message.HELLO`. Besides that, two variable bindings are created: the variable `message` is bound to the `message` attribute and the variable `m` is bound to the matched `Message` object itself.

The RHS (after `then`) or consequence part of the rule is written using the MVEL expression language, as declared by the rule's attribute `dialect`. After printing the content of the bound variable `message` to `System.out`, the rule changes the values of the `message` and `status` attributes of the `Message` object bound to `m`. This is done MVEL's `modify` statement, which allows you to apply a block of assignments in one statement, with the engine being automatically notified of the changes at the end of the block.

Example 7.7. HelloWorld: rule "Hello World"

    rule "Hello World"
          dialect "mvel"
      when
          m : Message( status == Message.HELLO, message : message )
      then
          System.out.println( message );
          modify ( m ) { message = "Goodbye cruel world",
                         status = Message.GOODBYE };
    end

We can set a breakpoint into the DRL, on the `modify` call, and inspect the Agenda view again during the execution of the rule's consequence. This time we start the execution via "Debug As" and "Drools application" and not by running a "Java application":

1. Open the class `org.drools.examples.HelloWorld` in your Eclipse IDE.

2. Right-click the class and select "Debug as..." and then "Drools application".

Now we can see that the other rule `"Good Bye"`, which uses the Java dialect, is activated and placed on the Agenda.

Figure 7.2. Hello World: rule "Hello World" Agenda View

The "Good Bye" rule, which specifies the "java" dialect, is similar to the "Hello World" rule except that it matches `Message` objects whose status is `Message.GOODBYE`.

Example 7.8. HelloWorld: rule "Good Bye"

    rule "Good Bye"
          dialect "java"
      when
          Message( status == Message.GOODBYE, message : message )
      then
          System.out.println( message );
    end

Remember the Java code where we used the `KnowledgeRuntimeLoggerFactory` method `newFileLogger` to create a `KnowledgeRuntimeLogger` and called `logger.close()` at the end. This created an audit log file that can be shown in the Audit view. We use the Audit view in many of the examples to demonstrate the example execution flow. In the view screen shot below we can see that the object is inserted, which creates an activation for the "Hello World" rule; the activation is then executed which updates the `Message` object causing the "Good Bye" rule to activate; finally the "Good Bye" rule also executes. Selecting an event in the Audit view highlights the origin event in green; therefore the "Activation created" event is highlighted in green as the origin of the "Activation executed" event.

Figure 7.3. Hello World: Audit View

## 7.3. State Example

This example is implemented in three different versions to demonstrate different ways of implementing the same basic behavior: forward chaining, i.e., the ability the engine has to evaluate, activate and fire rules in sequence, based on changes on the facts in the Working Memory.

### 7.3.1. Understanding the State Example

    Name: State Example
    Main class: org.drools.examples.state.StateExampleUsingSalience
    Module: drools-examples
    Type: Java application
    Rules file: StateExampleUsingSalience.drl
    Objective: Demonstrates basic rule use
      and Conflict Resolution for rule firing priority.

Each `State` class has fields for its name and its current state (see the class `org.drools.examples.state.State`). The two possible states for each objects are:

- `NOTRUN`

- `FINISHED`

Example 7.9. State Class

    public class State {

        public static final int NOTRUN   = 0;

        public static final int FINISHED = 1;

        private final PropertyChangeSupport changes =

            new PropertyChangeSupport( this );

        private String name;

        private int    state;

        ... setters and getters go here...

    }

Ignoring the `PropertyChangeSupport`, which will be explained later, we see the creation of four `State` objects named A, B, C and D. Initially their states are set to `NOTRUN`, which is default for the used constructor. Each instance is asserted in turn into the Session and then `fireAllRules()` is called.

Example 7.10. Salience State: Execution

    State a = new State( "A" );

    State b = new State( "B" );

    State c = new State( "C" );

    final State d = new State( "D" );

    // By setting dynamic to TRUE, Drools will use JavaBean

    // PropertyChangeListeners so you don't have to call modify or update().

    boolean dynamic = true;

    session.insert( a, dynamic );

    session.insert( b, dynamic );

    session.insert( c, dynamic );

    session.insert( d, dynamic );

    session.fireAllRules();

    session.dispose(); // Stateful rule session must always be disposed when finished

To execute the application:

1. Open the class `org.drools.examples.state.StateExampleUsingSalience` in your Eclipse IDE.

2. Right-click the class and select "Run as..." and then "Java application"

You will see the following output in the Eclipse console window:

Example 7.11. Salience State: Console Output

    A finished
    B finished
    C finished
    D finished

There are four rules in total. First, the `Bootstrap` rule fires, setting A to state `FINISHED`, which then causes B to change its state to `FINISHED`. C and D are both dependent on B, causing a conflict which is resolved by the salience values. Let's look at the way this was executed.

The best way to understand what is happening is to use the Audit Logging feature to graphically see the results of each operation. To view the Audit log generated by a run of this example:

1. If the Audit View is not visible, click on "Window" and then select "Show View", then "Other..." and "Drools" and finally "Audit View".

2. In the "Audit View" click the "Open Log" button and select the file "<drools-examples-dir>/log/state.log".

After that, the "Audit view" will look like the following screenshot:

Figure 7.4. Salience State Example Audit View

Reading the log in the "Audit View", top to bottom, we see every action and the corresponding changes in the Working Memory. This way we observe that the assertion of the State object A in the state `NOTRUN` activates the `Bootstrap` rule, while the assertions of the other `State` objects have no immediate effect.

Example 7.12. Salience State: Rule "Bootstrap"

    rule Bootstrap
        when
            a : State(name == "A", state == State.NOTRUN )
        then
            System.out.println(a.getName() + " finished" );
            a.setState( State.FINISHED );
    end

The execution of rule Bootstrap changes the state of A to `FINISHED`, which, in turn, activates rule "A to B".

Example 7.13. Salience State: Rule "A to B"

    rule "A to B"
        when
            State(name == "A", state == State.FINISHED )
            b : State(name == "B", state == State.NOTRUN )
        then
            System.out.println(b.getName() + " finished" );
            b.setState( State.FINISHED );
    end

The execution of rule "A to B" changes the state of B to `FINISHED`, which activates both, rules "B to C" and "B to D", placing their Activations onto the Agenda. From this moment on, both rules may fire and, therefore, they are said to be "in conflict". The conflict resolution strategy allows the engine's Agenda to decide which rule to fire. As rule "B to C" has the higher salience value (10 versus the default salience value of 0), it fires first, modifying object C to state `FINISHED`. The Audit view shown above reflects the modification of the `State` object in the rule "A to B", which results in two activations being in conflict. The Agenda view can also be used to investigate the state of the Agenda, with debug points being placed in the rules themselves and the Agenda view opened. The screen shot below shows the breakpoint in the rule "A to B" and the state of the Agenda with the two conflicting rules.

Figure 7.5. State Example Agenda View

Example 7.14. Salience State: Rule "B to C"

    rule "B to C"
            salience 10
        when
            State(name == "B", state == State.FINISHED )
            c : State(name == "C", state == State.NOTRUN )
        then
            System.out.println(c.getName() + " finished" );
            c.setState( State.FINISHED );
    end

Rule "B to D" fires last, modifying object D to state `FINISHED`.

Example 7.15. Salience State: Rule "B to D"

    rule "B to D"
        when
            State(name == "B", state == State.FINISHED )
            d : State(name == "D", state == State.NOTRUN )
        then
            System.out.println(d.getName() + " finished" );
            d.setState( State.FINISHED );
    end

There are no more rules to execute and so the engine stops.

Another notable concept in this example is the use of dynamic facts, based on `PropertyChangeListener` objects. As described in the documentation, in order for the engine to see and react to changes of fact properties, the application must tell the engine that changes occurred. This can be done explicitly in the rules by using the `modify` statement, or implicitly by letting the engine know that the facts implement `PropertyChangeSupport` as defined by the JavaBeans specification. This example demonstrates how to use `PropertyChangeSupport` to avoid the need for explicit `modify` statements in the rules. To make use of this feature, ensure that your facts implement `PropertyChangeSupport`, the same way the class `org.drools.example.State` does, and use the following code to insert the facts into the Working Memory:

Example 7.16. Inserting a Dynamic Fact

    // By setting dynamic to TRUE, Drools will use JavaBean

    // PropertyChangeListeners so you don't have to call modify or update().

    final boolean dynamic = true;

    session.insert( fact, dynamic );

When using `PropertyChangeListener` objects, each setter must implement a little extra code for the notification. Here is the setter for `state` in the class `org.drools.examples`:

:

Example 7.17. Setter Example with PropertyChangeSupport

    public void setState(final int newState) {

        int oldState = this.state;

        this.state = newState;

        this.changes.firePropertyChange( "state",

                                         oldState,

                                         newState );

    }

There are two other classes in this example: `StateExampleUsingAgendaGroup` and `StateExampleWithDynamicRules`. Both execute from A to B to C to D, as just shown. The `StateExampleUsingAgendaGroup` uses agenda-groups to control the rule conflict and which one fires first. `StateExampleWithDynamicRules` shows how an additional rule can be added to an already running Working Memory with all the existing data applying to it at runtime.

Agenda groups are a way to partition the Agenda into groups and to control which groups can execute. By default, all rules are in the agenda group "MAIN". The "agenda-group" attribute lets you specify a different agenda group for the rule. Initially, a Working Memory has its focus on the Agenda group "MAIN". A group's rules will only fire when the group receives the focus. This can be achieved either ny using the method by `setFocus()` or the rule attribute `auto-focus`. "auto-focus" means that the rule automatically sets the focus to its agenda group when the rule is matched and activated. It is this "auto-focus" that enables rule "B to C" to fire before "B to D".

Example 7.18. Agenda Group State Example: Rule "B to C"

    rule "B to C"
          agenda-group "B to C"
          auto-focus true
      when
          State(name == "B", state == State.FINISHED )
          c : State(name == "C", state == State.NOTRUN )
      then
          System.out.println(c.getName() + " finished" );
          c.setState( State.FINISHED );
          kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "B to D" ).setFocus();
    end

The rule "B to C" calls `setFocus()` on the agenda group "B to D", allowing its active rules to fire, which allows the rule "B to D" to fire.

Example 7.19. Agenda Group State Example: Rule "B to D"

    rule "B to D"
          agenda-group "B to D"
      when
          State(name == "B", state == State.FINISHED )
          d : State(name == "D", state == State.NOTRUN )
      then
          System.out.println(d.getName() + " finished" );
          d.setState( State.FINISHED );
    end

The example `StateExampleWithDynamicRules` adds another rule to the Rule Base after `fireAllRules()`. The added rule is just another state transition.

Example 7.20. Dynamic State Example: Rule "D to E"

    rule "D to E"
      when
          State(name == "D", state == State.FINISHED )
          e : State(name == "E", state == State.NOTRUN )
      then
          System.out.println(e.getName() + " finished" );
          e.setState( State.FINISHED );
    end

This produces the following expected output:

Example 7.21. Dynamic Sate Example Output

    A finished
    B finished
    C finished
    D finished
    E finished

## 7.4. Fibonacci Example

    Name: Fibonacci
    Main class: org.drools.examples.fibonacci.FibonacciExample
    Module: drools-examples
    Type: Java application
    Rules file: Fibonacci.drl
    Objective: Demonstrates Recursion,
      the CE not and cross product matching

The Fibonacci Numbers (see http://en.wikipedia.org/wiki/Fibonacci_number) discovered by Leonardo of Pisa (see http://en.wikipedia.org/wiki/Fibonacci) is a sequence that starts with 0 and 1. The next Fibonacci number is obtained by adding the two preceding Fibonacci numbers. The Fibonacci sequence begins with 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946,... The Fibonacci Example demonstrates recursion and conflict resolution with salience values.

The single fact class `Fibonacci` is used in this example. It has two fields, sequence and value. The sequence field is used to indicate the position of the object in the Fibonacci number sequence. The value field shows the value of that Fibonacci object for that sequence position, using -1 to indicate a value that still needs to be computed.

Example 7.22. Fibonacci Class

    public static class Fibonacci {

        private int  sequence;

        private long value;

        public Fibonacci( final int sequence ) {

            this.sequence = sequence;

            this.value = -1;

        }

        ... setters and getters go here...

    }

Execute the example:

1. Open the class `org.drools.examples.fibonacci.FibonacciExample` in your Eclipse IDE.

2. Right-click the class and select "Run as..." and then "Java application"

Eclipse shows the following output in its console window (with "...snip..." indicating lines that were removed to save space):

Example 7.23. Fibonacci Example: Console Output

    recurse for 50
    recurse for 49
    recurse for 48
    recurse for 47
    ...snip...
    recurse for 5
    recurse for 4
    recurse for 3
    recurse for 2
    1 == 1
    2 == 1
    3 == 2
    4 == 3
    5 == 5
    6 == 8
    ...snip...
    47 == 2971215073
    48 == 4807526976
    49 == 7778742049
    50 == 12586269025

To kick this off from Java we only insert a single Fibonacci object, with a sequence field of 50. A recursive rule is then used to insert the other 49 `Fibonacci` objects. This example doesn't use `PropertyChangeSupport`. It uses the MVEL dialect, which means we can use the `modify` keyword, which allows a block setter action which also notifies the engine of changes.

Example 7.24. Fibonacci Example: Execution

    ksession.insert( new Fibonacci( 50 ) );

    ksession.fireAllRules();

The rule Recurse is very simple. It matches each asserted `Fibonacci` object with a value of -1, creating and asserting a new `Fibonacci` object with a sequence of one less than the currently matched object. Each time a Fibonacci object is added while the one with a sequence field equal to 1 does not exist, the rule re-matches and fires again. The `not` conditional element is used to stop the rule's matching once we have all 50 Fibonacci objects in memory. The rule also has a salience value, because we need to have all 50 `Fibonacci` objects asserted before we execute the Bootstrap rule.

Example 7.25. Fibonacci Example: Rule "Recurse"

    rule Recurse
        salience 10
        when
            f : Fibonacci ( value == -1 )
            not ( Fibonacci ( sequence == 1 ) )
        then
            insert( new Fibonacci( f.sequence - 1 ) );
            System.out.println( "recurse for " + f.sequence );
    end

The Audit view shows the original assertion of the `Fibonacci` object with a sequence field of 50, done from Java code. From there on, the Audit view shows the continual recursion of the rule, where each asserted `Fibonacci` object causes the Recurse rule to become activated and to fire again.

Figure 7.6. Fibonacci Example: "Recurse" Audit View 1

When a `Fibonacci` object with a sequence field of 2 is asserted the "Bootstrap" rule is matched and activated along with the "Recurse" rule. Note the multi-restriction on field `sequence`, testing for equality with 1 or 2.

Example 7.26. Fibonacci Example: Rule "Bootstrap"

    rule Bootstrap
        when
            f : Fibonacci( sequence == 1 || == 2, value == -1 ) // multi-restriction
        then
            modify ( f ){ value = 1 };
            System.out.println( f.sequence + " == " + f.value );
    end

At this point the Agenda looks as shown below. However, the "Bootstrap" rule does not fire because the "Recurse" rule has a higher salience.

Figure 7.7. Fibonacci Example: "Recurse" Agenda View 1

When a `Fibonacci` object with a sequence of 1 is asserted the Bootstrap rule is matched again, causing two activations for this rule. Note that the "Recurse" rule does not match and activate because the `not` conditional element stops the rule's matching as soon as a `Fibonacci` object with a sequence of 1 exists.

Figure 7.8. Fibonacci Example: "Recurse" Agenda View 2

Once we have two `Fibonacci` objects with values not equal to -1 the "Calculate" rule is able to match. It was the "Bootstrap" rule that set the objects with sequence 1 and 2 to values of 1. At this point we have 50 Fibonacci objects in the Working Memory. Now we need to select a suitable triple to calculate each of their values in turn. Using three Fibonacci patterns in a rule without field constraints to confine the possible cross products would result in 50x49x48 possible combinations, leading to about 125,000 possible rule firings, most of them incorrect. The "Calculate" rule uses field constraints to correctly constraint the thee Fibonacci patterns in the correct order; this technique is called cross product matching. The first pattern finds any Fibonacci with a value != -1 and binds both the pattern and the field. The second Fibonacci does this, too, but it adds an additional field constraint to ensure that its sequence is greater by one than the Fibonacci bound to `f1`. When this rule fires for the first time, we know that only sequences 1 and 2 have values of 1, and the two constraints ensure that `f1` references sequence 1 and `f2` references sequence 2. The final pattern finds the Fibonacci with a value equal to -1 and with a sequence one greater than `f2`. At this point, we have three `Fibonacci` objects correctly selected from the available cross products, and we can calculate the value for the third `Fibonacci` object that's bound to `f3`.

Example 7.27. Fibonacci Example: Rule "Calculate"

    rule Calculate
        when
            // Bind f1 and s1
            f1 : Fibonacci( s1 : sequence, value != -1 )
            // Bind f2 and v2; refer to bound variable s1
            f2 : Fibonacci( sequence == (s1 + 1), v2 : value != -1 )
            // Bind f3 and s3; alternative reference of f2.sequence
            f3 : Fibonacci( s3 : sequence == (f2.sequence + 1 ), value == -1 )
        then
            // Note the various referencing techniques.
            modify ( f3 ) { value = f1.value + v2 };
            System.out.println( s3 + " == " + f3.value );
    end

The `modify` statement updated the value of the `Fibonacci` object bound to `f3`. This means we now have another new Fibonacci object with a value not equal to -1, which allows the "Calculate" rule to rematch and calculate the next Fibonacci number. The Audit view below shows how the firing of the last "Bootstrap" modifies the `Fibonacci` object, enabling the "Calculate" rule to match, which then modifies another Fibonacci object allowing the "Calculate" rule to match again. This continues till the value is set for all `Fibonacci` objects.

Figure 7.9. Fibonacci Example: "Bootstrap" Audit View

## 7.5. Banking Tutorial

    Name: BankingTutorial
    Main class: org.drools.tutorials.banking.BankingExamplesApp.java
    Module: drools-examples
    Type: Java application
    Rules file: org.drools.tutorials.banking.*.drl
    Objective: Demonstrate pattern matching, basic sorting and calculation rules.

This tutorial demonstrates the process of developing a complete personal banking application to handle credits and debits on multiple accounts. It uses a set of design patterns that have been created for the process.

The class `RuleRunner` is a simple harness to execute one or more DRL files against a set of data. It compiles the Packages and creates the Knowledge Base for each execution, allowing us to easily execute each scenario and inspect the outputs. In reality this is not a good solution for a production system, where the Knowledge Base should be built just once and cached, but for the purposes of this tutorial it shall suffice.

Example 7.28. Banking Tutorial: RuleRunner

    public class RuleRunner {

        public RuleRunner() {

        }

        public void runRules(String[] rules,

                             Object[] facts) throws Exception {

            KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();

            KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

            for ( int i = 0; i < rules.length; i++ ) {

                String ruleFile = rules[i];

                System.out.println( "Loading file: " + ruleFile );

                kbuilder.add( ResourceFactory.newClassPathResource( ruleFile,

                                                                    RuleRunner.class ),

                              ResourceType.DRL );

            }

            Collection<KnowledgePackage> pkgs = kbuilder.getKnowledgePackages();

            kbase.addKnowledgePackages( pkgs );

            StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

            for ( int i = 0; i < facts.length; i++ ) {

                Object fact = facts[i];

                System.out.println( "Inserting fact: " + fact );

                ksession.insert( fact );

            }

            ksession.fireAllRules();

        }

    }

The first of our sample Java classes loads and executes a single DRL file, `Example.drl`, but without inserting any data.

Example 7.29. Banking Tutorial : Java Example1

    public class Example1 {

        public static void main(String[] args) throws Exception {

            new RuleRunner().runRules( new String[] { "Example1.drl" },

                                       new Object[0] );

        }

    }

The first simple rule to execute has a single `eval` condition that will always be true, so that this rule will match and fire, once, after the start.

Example 7.30. Banking Tutorial: Rule in Example1.drl

    rule "Rule 01"
        when
            eval( 1==1 )
        then
            System.out.println( "Rule 01 Works" );
    end

The output for the rule is below, showing that the rule matches and executes the single print statement.

Example 7.31. Banking Tutorial: Output of Example1.java

    Loading file: Example1.drl
    Rule 01 Works

The next step is to assert some simple facts and print them out.

Example 7.32. Banking Tutorial: Java Example2

    public class Example2 {

        public static void main(String[] args) throws Exception {

            Number[] numbers = new Number[] {wrap(3), wrap(1), wrap(4), wrap(1), wrap(5)};

            new RuleRunner().runRules( new String[] { "Example2.drl" },

                                       numbers );

        }

        private static Integer wrap( int i ) {

            return new Integer(i);

        }

    }

This doesn't use any specific facts but instead asserts a set of `java.lang.Integer` objects. This is not considered "best practice" as a number is not a useful fact, but we use it here to demonstrate basic techniques before more complexity is added.

Now we will create a simple rule to print out these numbers.

Example 7.33. Banking Tutorial: Rule in Example2.drl

    rule "Rule 02"
        when
            Number( $intValue : intValue )
        then
            System.out.println( "Number found with value: " + $intValue );
    end

Once again, this rule does nothing special. It identifies any facts that are `Number` objects and prints out the values. Notice the use of the abstract class `Number`: we inserted `Integer` objects but we now look for any kind of number. The pattern matching engine is able to match interfaces and superclasses of asserted objects.

The output shows the DRL being loaded, the facts inserted and then the matched and fired rules. We can see that each inserted number is matched and fired and thus printed.

Example 7.34. Banking Tutorial: Output of Example2.java

    Loading file: Example2.drl
    Inserting fact: 3
    Inserting fact: 1
    Inserting fact: 4
    Inserting fact: 1
    Inserting fact: 5
    Number found with value: 5
    Number found with value: 1
    Number found with value: 4
    Number found with value: 1
    Number found with value: 3

There are certainly many better ways to sort numbers than using rules, but since we will need to apply some cashflows in date order when we start looking at banking rules we'll develop simple rule based sorting technique.

Example 7.35. Banking Tutorial: Example3.java

    public class Example3 {

        public static void main(String[] args) throws Exception {

            Number[] numbers = new Number[] {wrap(3), wrap(1), wrap(4), wrap(1), wrap(5)};

            new RuleRunner().runRules( new String[] { "Example3.drl" },

                                       numbers );

        }

        private static Integer wrap(int i) {

            return new Integer(i);

        }

    }

Again we insert our `Integer` objects, but this time the rule is slightly different:

Example 7.36. Banking Tutorial: Rule in Example3.drl

    rule "Rule 03"
        when
            $number : Number( )
            not Number( intValue < $number.intValue )
        then
            System.out.println("Number found with value: " + $number.intValue() );
            retract( $number );
    end

The first line of the rule identifies a `Number` and extracts the value. The second line ensures that there does not exist a smaller number than the one found by the first pattern. We might expect to match only one number - the smallest in the set. However, the retraction of the number after it has been printed means that the smallest number has been removed, revealing the next smallest number, and so on.

The resulting output shows that the numbers are now sorted numerically.

Example 7.37. Banking Tutorial: Output of Example3.java

    Loading file: Example3.drl
    Inserting fact: 3
    Inserting fact: 1
    Inserting fact: 4
    Inserting fact: 1
    Inserting fact: 5
    Number found with value: 1
    Number found with value: 1
    Number found with value: 3
    Number found with value: 4
    Number found with value: 5

We are ready to start moving towards our personal accounting rules. The first step is to create a `Cashflow` object.

Example 7.38. Banking Tutorial: Class Cashflow

    public class Cashflow {

        private Date   date;

        private double amount;

        public Cashflow() {

        }

        public Cashflow(Date date, double amount) {

            this.date = date;

            this.amount = amount;

        }

        public Date getDate() {

            return date;

        }

        public void setDate(Date date) {

            this.date = date;

        }

        public double getAmount() {

            return amount;

        }

        public void setAmount(double amount) {

            this.amount = amount;

        }

        public String toString() {

            return "Cashflow[date=" + date + ",amount=" + amount + "]";

        }

    }

Class `Cashflow` has two simple attributes, a date and an amount. (Note that using the type `double` for monetary units is generally not a good idea because floating point numbers cannot represent most numbers accurately.) There is also an overloaded constructor to set the values, and a method `toString` to print a cashflow. The Java code of `Example4.java` inserts five Cashflow objects, with varying dates and amounts.

Example 7.39. Banking Tutorial: Example4.java

    public class Example4 {

        public static void main(String[] args) throws Exception {

            Object[] cashflows = {

                new Cashflow(new SimpleDate("01/01/2007"), 300.00),

                new Cashflow(new SimpleDate("05/01/2007"), 100.00),

                new Cashflow(new SimpleDate("11/01/2007"), 500.00),

                new Cashflow(new SimpleDate("07/01/2007"), 800.00),

                new Cashflow(new SimpleDate("02/01/2007"), 400.00),

            };

            new RuleRunner().runRules( new String[] { "Example4.drl" },

                                       cashflows );

        }

    }

The convenience class `SimpleDate` extends `java.util.Date`, providing a constructor taking a String as input and defining a date format. The code is listed below

Example 7.40. Banking Tutorial: Class SimpleDate

    public class SimpleDate extends Date {

        private static final SimpleDateFormat format = new SimpleDateFormat("dd/MM/yyyy");

        public SimpleDate(String datestr) throws Exception {

            setTime(format.parse(datestr).getTime());

        }

    }

Now, let’s look at `Example4.drl` to see how we print the sorted `Cashflow` objects:

Example 7.41. Banking Tutorial: Rule in Example4.drl

    rule "Rule 04"
        when
            $cashflow : Cashflow( $date : date, $amount : amount )
            not Cashflow( date < $date)
        then
            System.out.println("Cashflow: "+$date+" :: "+$amount);
            retract($cashflow);
    end

Here, we identify a `Cashflow` and extract the date and the amount. In the second line of the rule we ensure that there is no Cashflow with an earlier date than the one found. In the consequence, we print the `Cashflow` that satisfies the rule and then retract it, making way for the next earliest `Cashflow`. So, the output we generate is:

Example 7.42. Banking Tutorial: Output of Example4.java

    Loading file: Example4.drl
    Inserting fact: Cashflow[date=Mon Jan 01 00:00:00 GMT 2007,amount=300.0]
    Inserting fact: Cashflow[date=Fri Jan 05 00:00:00 GMT 2007,amount=100.0]
    Inserting fact: Cashflow[date=Thu Jan 11 00:00:00 GMT 2007,amount=500.0]
    Inserting fact: Cashflow[date=Sun Jan 07 00:00:00 GMT 2007,amount=800.0]
    Inserting fact: Cashflow[date=Tue Jan 02 00:00:00 GMT 2007,amount=400.0]
    Cashflow: Mon Jan 01 00:00:00 GMT 2007 :: 300.0
    Cashflow: Tue Jan 02 00:00:00 GMT 2007 :: 400.0
    Cashflow: Fri Jan 05 00:00:00 GMT 2007 :: 100.0
    Cashflow: Sun Jan 07 00:00:00 GMT 2007 :: 800.0
    Cashflow: Thu Jan 11 00:00:00 GMT 2007 :: 500.0

Next, we extend our `Cashflow`, resulting in a `TypedCashflow` which can be a credit or a debit operation. (Normally, we would just add this to the `Cashflow` type, but we use extension to keep the previous version of the class intact.)

Example 7.43. Banking Tutorial: Class TypedCashflow

    public class TypedCashflow extends Cashflow {

        public static final int CREDIT = 0;

        public static final int DEBIT  = 1;

        private int             type;

        public TypedCashflow() {

        }

        public TypedCashflow(Date date, int type, double amount) {

            super( date, amount );

            this.type = type;

        }

        public int getType() {

            return type;

        }

        public void setType(int type) {

            this.type = type;

        }

        public String toString() {

            return "TypedCashflow[date=" + getDate() +

                   ",type=" + (type == CREDIT ? "Credit" : "Debit") +

                   ",amount=" + getAmount() + "]";

        }

    }

There are lots of ways to improve this code, but for the sake of the example this will do.

Now let's create Example5, a class for running our code.

Example 7.44. Banking Tutorial: Example5.java

    public class Example5 {

        public static void main(String[] args) throws Exception {

            Object[] cashflows = {

                new TypedCashflow(new SimpleDate("01/01/2007"),

                                  TypedCashflow.CREDIT, 300.00),

                new TypedCashflow(new SimpleDate("05/01/2007"),

                                  TypedCashflow.CREDIT, 100.00),

                new TypedCashflow(new SimpleDate("11/01/2007"),

                                  TypedCashflow.CREDIT, 500.00),

                new TypedCashflow(new SimpleDate("07/01/2007"),

                                  TypedCashflow.DEBIT, 800.00),

                new TypedCashflow(new SimpleDate("02/01/2007"),

                                  TypedCashflow.DEBIT, 400.00),

            };

            new RuleRunner().runRules( new String[] { "Example5.drl" },

                                       cashflows );

        }

    }

Here, we simply create a set of `Cashflow` objects which are either credit or debit operations. We supply them and `Example5.drl` to the RuleEngine.

Now, let’s look at a rule printing the sorted `Cashflow` objects.

Example 7.45. Banking Tutorial: Rule in Example5.drl

    rule "Rule 05"
        when
            $cashflow : TypedCashflow( $date : date,
                                       $amount : amount,
                                       type == TypedCashflow.CREDIT )
            not TypedCashflow( date < $date,
                               type == TypedCashflow.CREDIT )
        then
            System.out.println("Credit: "+$date+" :: "+$amount);
            retract($cashflow);
    end

Here, we identify a `Cashflow` fact with a type of `CREDIT` and extract the date and the amount. In the second line of the rule we ensure that there is no `Cashflow` of the same type with an earlier date than the one found. In the consequence, we print the cashflow satisfying the patterns and then retract it, making way for the next earliest cashflow of type `CREDIT`.

So, the output we generate is

Example 7.46. Banking Tutorial: Output of Example5.java

    Loading file: Example5.drl
    Inserting fact: TypedCashflow[date=Mon Jan 01 00:00:00 GMT 2007,type=Credit,amount=300.0]
    Inserting fact: TypedCashflow[date=Fri Jan 05 00:00:00 GMT 2007,type=Credit,amount=100.0]
    Inserting fact: TypedCashflow[date=Thu Jan 11 00:00:00 GMT 2007,type=Credit,amount=500.0]
    Inserting fact: TypedCashflow[date=Sun Jan 07 00:00:00 GMT 2007,type=Debit,amount=800.0]
    Inserting fact: TypedCashflow[date=Tue Jan 02 00:00:00 GMT 2007,type=Debit,amount=400.0]
    Credit: Mon Jan 01 00:00:00 GMT 2007 :: 300.0
    Credit: Fri Jan 05 00:00:00 GMT 2007 :: 100.0
    Credit: Thu Jan 11 00:00:00 GMT 2007 :: 500.0

Continuing our banking exercise, we are now going to process both credits and debits on two bank accounts, calculating the account balance. In order to do this, we create two separate `Account` objects and inject them into the `Cashflows` objects before passing them to the Rule Engine. The reason for this is to provide easy access to the correct account without having to resort to helper classes. Let’s take a look at the `Account` class first. This is a simple Java object with an account number and balance:

Example 7.47. Banking Tutorial: Class Account

    public class Account {

        private long   accountNo;

        private double balance = 0;

        public Account() {

        }

        public Account(long accountNo) {

            this.accountNo = accountNo;

        }

        public long getAccountNo() {

            return accountNo;

        }

        public void setAccountNo(long accountNo) {

            this.accountNo = accountNo;

        }

        public double getBalance() {

            return balance;

        }

        public void setBalance(double balance) {

            this.balance = balance;

        }

        public String toString() {

            return "Account[" + "accountNo=" + accountNo + ",balance=" + balance + "]";

        }

    }

Now let’s extend our `TypedCashflow`, resulting in `AllocatedCashflow`, to include an `Account` reference.

Example 7.48. Banking Tutorial: Class AllocatedCashflow

    public class AllocatedCashflow extends TypedCashflow {

        private Account account;

        public AllocatedCashflow() {

        }

        public AllocatedCashflow(Account account, Date date, int type, double amount) {

            super( date, type, amount );

            this.account = account;

        }

        public Account getAccount() {

            return account;

        }

        public void setAccount(Account account) {

            this.account = account;

        }

        public String toString() {

            return "AllocatedCashflow[" +

                   "account=" + account +

                   ",date=" + getDate() +

                   ",type=" + (getType() == CREDIT ? "Credit" : "Debit") +

                   ",amount=" + getAmount() + "]";

        }

    }

The Java code of `Example5.java` creates two `Account` objects and passes one of them into each cashflow, in the constructor call.

Example 7.49. Banking Tutorial: Example5.java

    public class Example6 {

        public static void main(String[] args) throws Exception {

            Account acc1 = new Account(1);

            Account acc2 = new Account(2);

            Object[] cashflows = {

                new AllocatedCashflow(acc1,new SimpleDate("01/01/2007"),

                                      TypedCashflow.CREDIT, 300.00),

                new AllocatedCashflow(acc1,new SimpleDate("05/02/2007"),

                                      TypedCashflow.CREDIT, 100.00),

                new AllocatedCashflow(acc2,new SimpleDate("11/03/2007"),

                                      TypedCashflow.CREDIT, 500.00),

                new AllocatedCashflow(acc1,new SimpleDate("07/02/2007"),

                                      TypedCashflow.DEBIT,  800.00),

                new AllocatedCashflow(acc2,new SimpleDate("02/03/2007"),

                                      TypedCashflow.DEBIT,  400.00),

                new AllocatedCashflow(acc1,new SimpleDate("01/04/2007"),

                                      TypedCashflow.CREDIT, 200.00),

                new AllocatedCashflow(acc1,new SimpleDate("05/04/2007"),

                                      TypedCashflow.CREDIT, 300.00),

                new AllocatedCashflow(acc2,new SimpleDate("11/05/2007"),

                                      TypedCashflow.CREDIT, 700.00),

                new AllocatedCashflow(acc1,new SimpleDate("07/05/2007"),

                                      TypedCashflow.DEBIT,  900.00),

                new AllocatedCashflow(acc2,new SimpleDate("02/05/2007"),

                                      TypedCashflow.DEBIT,  100.00)

            };

            new RuleRunner().runRules( new String[] { "Example6.drl" },

                                       cashflows );

        }

    }

Now, let’s look at the rule in `Example6.drl` to see how we apply each cashflow in date order and calculate and print the balance.

Example 7.50. Banking Tutorial: Rule in Example6.drl

    rule "Rule 06 - Credit"
        when
            $cashflow : AllocatedCashflow( $account : account,
                                           $date : date,
                                           $amount : amount,
                                           type == TypedCashflow.CREDIT )
            not AllocatedCashflow( account == $account, date < $date)
        then
            System.out.println("Credit: " + $date + " :: " + $amount);
            $account.setBalance($account.getBalance()+$amount);
            System.out.println("Account: " + $account.getAccountNo() +
                               " - new balance: " + $account.getBalance());
            retract($cashflow);
    end

    rule "Rule 06 - Debit"
        when
            $cashflow : AllocatedCashflow( $account : account,
                                $date : date,
                                $amount : amount,
                                type == TypedCashflow.DEBIT )
            not AllocatedCashflow( account == $account, date < $date)
        then
            System.out.println("Debit: " + $date + " :: " + $amount);
            $account.setBalance($account.getBalance() - $amount);
            System.out.println("Account: " + $account.getAccountNo() +
                               " - new balance: " + $account.getBalance());
            retract($cashflow);
    end

Although we have separate rules for credits and debits, but we do not specify a type when checking for earlier cashflows. This is so that all cashflows are applied in date order, regardless of the cashflow type. In the conditions we identify the account to work with, and in the consequences we update it with the cashflow amount.

Example 7.51. Banking Tutorial: Output of Example6.java

    Loading file: Example6.drl
    Inserting fact: AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Mon Jan 01 00:00:00 GMT 2007,type=Credit,amount=300.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Mon Feb 05 00:00:00 GMT 2007,type=Credit,amount=100.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Sun Mar 11 00:00:00 GMT 2007,type=Credit,amount=500.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Wed Feb 07 00:00:00 GMT 2007,type=Debit,amount=800.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Fri Mar 02 00:00:00 GMT 2007,type=Debit,amount=400.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Sun Apr 01 00:00:00 BST 2007,type=Credit,amount=200.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Thu Apr 05 00:00:00 BST 2007,type=Credit,amount=300.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Fri May 11 00:00:00 BST 2007,type=Credit,amount=700.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Mon May 07 00:00:00 BST 2007,type=Debit,amount=900.0]
    Inserting fact: AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Wed May 02 00:00:00 BST 2007,type=Debit,amount=100.0]
    Debit: Fri Mar 02 00:00:00 GMT 2007 :: 400.0
    Account: 2 - new balance: -400.0
    Credit: Sun Mar 11 00:00:00 GMT 2007 :: 500.0
    Account: 2 - new balance: 100.0
    Debit: Wed May 02 00:00:00 BST 2007 :: 100.0
    Account: 2 - new balance: 0.0
    Credit: Fri May 11 00:00:00 BST 2007 :: 700.0
    Account: 2 - new balance: 700.0
    Credit: Mon Jan 01 00:00:00 GMT 2007 :: 300.0
    Account: 1 - new balance: 300.0
    Credit: Mon Feb 05 00:00:00 GMT 2007 :: 100.0
    Account: 1 - new balance: 400.0
    Debit: Wed Feb 07 00:00:00 GMT 2007 :: 800.0
    Account: 1 - new balance: -400.0
    Credit: Sun Apr 01 00:00:00 BST 2007 :: 200.0
    Account: 1 - new balance: -200.0
    Credit: Thu Apr 05 00:00:00 BST 2007 :: 300.0
    Account: 1 - new balance: 100.0
    Debit: Mon May 07 00:00:00 BST 2007 :: 900.0
    Account: 1 - new balance: -800.0

## 7.6. Pricing Rule Decision Table Example

The Pricing Rule decision table demonstrates the use of a decision table in a spreadsheet, in Excel's XLS format, in calculating the retail cost of an insurance policy. The purpose of the provide set of rules is to calculate a base price and a discount for a car driver applying for a specific policy. The driver's age, history and the policy type all contribute to what the basic premium is, and an additional chunk of rules deals with refining this with a discount percentage.

    Name: Example Policy Pricing
    Main class: org.drools.examples.decisiontable.PricingRuleDTExample
    Module: drools-examples
    Type: Java application
    Rules file: ExamplePolicyPricing.xls
    Objective: demonstrate spreadsheet-based decision tables.

### 7.6.1. Executing the example

Open the file `PricingRuleDTExample.java` and execute it as a Java application. It should produce the following output in the Console window:

    Cheapest possible
    BASE PRICE IS: 120
    DISCOUNT IS: 20

The code to execute the example follows the usual pattern. The rules are loaded, the facts inserted and a Stateless Session is created. What is different is how the rules are added.

    DecisionTableConfiguration dtableconfiguration =

        KnowledgeBuilderFactory.newDecisionTableConfiguration();

            dtableconfiguration.setInputType( DecisionTableInputType.XLS );

            KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

            Resource xlsRes = ResourceFactory.newClassPathResource( "ExamplePolicyPricing.xls",

                                                                    getClass() );

            kbuilder.add( xlsRes,

                          ResourceType.DTABLE,

                          dtableconfiguration );

Note the use of the `DecisionTableConfiguration` object. Its input type is set to `DecisionTableInputType.XLS`. If you use the BRMS, all this is of course taken care of for you.

There are two fact types used in this example, `Driver` and `Policy`. Both are used with their default values. The `Driver` is 30 years old, has had no prior claims and currently has a risk profile of `LOW`. The `Policy` being applied for is `COMPREHENSIVE`, and it has not yet been approved.

### 7.6.2. The decision table

In this decision table, each row is a rule, and each column is a condition or an action.

Figure 7.10. Decision table configuration

Referring to the spreadsheet show above, we have the `RuleSet` declaration, which provides the package name. There are also other optional items you can have here, such as `Variables` for global variables, and `Imports` for importing classes. In this case, the namespace of the rules is the same as the fact classes we are using, so we can omit it.

Moving further down, we can see the `RuleTable` declaration. The name after this (Pricing bracket) is used as the prefix for all the generated rules. Below that, we have "CONDITION or ACTION", indicating the purpose of the column, i.e., whether it forms part of the condition or the consequence of the rule that will be generated.

You can see that there is a driver, his data spanned across three cells, which means that the template expressions below it apply to that fact. We observe the driver's age range (which uses `$1` and `$2` with comma-separated values), `locationRiskProfile`, and `priorClaims` in the respective columns. In the action columns, we are set the policy base price and log a message.

Figure 7.11. Base price calculation

In the preceding spreadsheet section, there are broad category brackets, indicated by the comment in the leftmost column. As we know the details of our drivers and their policies, we can tell (with a bit of thought) that they should match row number 18, as they have no prior accidents, and are 30 years old. This gives us a base price of 120.

Figure 7.12. Discount calculation

The above section contains the conditions for the discount we might grant our driver. The discount results from the `Age` bracket, the number of prior claims, and the policy type. In our case, the driver is 30, with no prior claims, and is applying for a `COMPREHENSIVE` policy, which means we can give a discount of 20%. Note that this is actually a separate table, but in the same worksheet, so that different templates apply.

It is important to note that decision tables generate rules. This means they aren't simply top-down logic, but more a means to capture data resulting in rules. This is a subtle difference that confuses some people. The evaluation of the rules is not necessarily in the given order, since all the normal mechanics of the rule engine still apply.

## 7.7. Pet Store Example

    Name: Pet Store
    Main class: org.drools.examples.petstore.PetStoreExample
    Module: drools-examples
    Type: Java application
    Rules file: PetStore.drl
    Objective: Demonstrate use of Agenda Groups, Global Variables and integration with a GUI,
    including callbacks from within the rules

The Pet Store example shows how to integrate Rules with a GUI, in this case a Swing based desktop application. Within the rules file, it demonstrates how to use Agenda groups and auto-focus to control which of a set of rules is allowed to fire at any given time. It also illustrates the mixing of the Java and MVEL dialects within the rules, the use of accumulate functions and the way of calling Java functions from within the ruleset.

All of the Java code is contained in one file, `PetStore.java`, defining the following principal classes (in addition to several classes to handle Swing Events):

- `Petstore` contains the `main()` method that we will look at shortly.

- `PetStoreUI` is responsible for creating and displaying the Swing based GUI. It contains several smaller classes, mainly for responding to various GUI events such as mouse button clicks.

- `TableModel` holds the table data. Think of it as a JavaBean that extends the Swing class `AbstractTableModel`.

- `CheckoutCallback` allows the GUI to interact with the Rules.

- `Ordershow` keeps the items that we wish to buy.

- `Purchase` stores details of the order and the products we are buying.

- `Product` is a JavaBean holding details of the product available for purchase, and its price.

Much of the Java code is either plain JavaBeans or Swing-based. Only a few Swing-related points will be discussed in this section, but a good tutorial about Swing components can be found at Sun's Swing website, in http://java.sun.com/docs/books/tutorial/uiswing/.

The pieces of Java code in `Petstore.java` that relate to rules and facts are shown below.

Example 7.52. Creating the PetStore RuleBase in PetStore.main

    KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    kbuilder.add( ResourceFactory.newClassPathResource( "PetStore.drl",

                                                        PetStore.class ),

                  ResourceType.DRL );

    KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();

    kbase.addKnowledgePackages( kbuilder.getKnowledgePackages() );

    // Create the stock.

    Vector<Product> stock = new Vector<Product>();

    stock.add( new Product( "Gold Fish", 5 ) );

    stock.add( new Product( "Fish Tank", 25 ) );

    stock.add( new Product( "Fish Food", 2 ) );

    // A callback is responsible for populating the

    // Working Memory and for firing all rules.

    PetStoreUI ui = new PetStoreUI( stock,

                                    new CheckoutCallback( kbase ) );

    ui.createAndShowGUI();

The code shown above loads the rules from a DRL file on the classpath. Unlike other examples where the facts are asserted and fired straight away, this example defers this step to later. The way it does this is via the second last line where a `PetStoreUI` object is created using a constructor accepting the `Vector` object `stock` collecting our products, and an instance of the `CheckoutCallback` class containing the Rule Base that we have just loaded.

The Java code that fires the rules is within the `CheckoutCallBack.checkout()` method. This is triggered (eventually) when the Checkout button is pressed by the user.

Example 7.53. Firing the Rules - extract from CheckoutCallBack.checkout()

    public String checkout(JFrame frame, List<Product> items) {

        Order order = new Order();

        // Iterate through list and add to cart

        for ( Product p: items ) {

            order.addItem( new Purchase( order, p ) );

        }

        // Add the JFrame to the ApplicationData to allow for user interaction

        StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

        ksession.setGlobal( "frame", frame );

        ksession.setGlobal( "textArea", this.output );

        ksession.insert( new Product( "Gold Fish", 5 ) );

        ksession.insert( new Product( "Fish Tank", 25 ) );

        ksession.insert( new Product( "Fish Food", 2 ) );

        ksession.insert( new Product( "Fish Food Sample", 0 ) );

        ksession.insert( order );

        ksession.fireAllRules();

        // Return the state of the cart

        return order.toString();

    }

Two items get passed into this method. One is the handle to the `JFrame` Swing component surrounding the output text frame, at the bottom of the GUI. The second is a list of order items; this comes from the `TableModel` storing the information from the "Table" area at the top right section of the GUI.

The for loop transforms the list of order items coming from the GUI into the `Order` JavaBean, also contained in the file `PetStore.java`. Note that it would be possible to refer to the Swing dataset directly within the rules, but it is better coding practice to do it this way, using simple Java objects. It means that we are not tied to Swing if we wanted to transform the sample into a Web application.

It is important to note that all state in this example is stored in the Swing components, and that the rules are effectively stateless. Each time the "Checkout" button is pressed, this code copies the contents of the Swing `TableModel` into the Session's Working Memory.

Within this code, there are nine calls to the Working Memory. The first of these creates a new Working Memory, as a Stateful Knowledge Session from the Knowledge Base. Remember that we passed in this Knowledge Base when we created the `CheckoutCallBack` class in the `main()` method. The next two calls pass in two objects that we will hold as global variables in the rules: the Swing text area and the Swing frame used for writing messages.

More inserts put information on products into the Working Memory, as well as the order list. The final call is the standard `fireAllRules()`. Next, we look at what this method causes to happen within the rules file.

Example 7.54. Package, Imports, Globals and Dialect: extract from PetStore.drl

    package org.drools.examples

    import org.drools.core.WorkingMemory

    import org.drools.examples.petstore.PetStoreExample.Order

    import org.drools.examples.petstore.PetStoreExample.Purchase

    import org.drools.examples.petstore.PetStoreExample.Product

    import java.util.ArrayList

    import javax.swing.JOptionPane;

    import javax.swing.JFrame

    global JFrame frame

    global javax.swing.JTextArea textArea

The first part of file `PetStore.drl` contains the standard package and import statements to make various Java classes available to the rules. New to us are the two globals `frame` and `textArea`. They hold references to the Swing components `JFrame` and `JTextArea` components that were previously passed on by the Java code calling the `setGlobal()` method. Unlike variables in rules, which expire as soon as the rule has fired, global variables retain their value for the lifetime of the Session.

The next extract from the file `PetStore.drl` contains two functions that are referenced by the rules that we will look at shortly.

Example 7.55. Java Functions in the Rules: extract from PetStore.drl

    function void doCheckout(JFrame frame, WorkingMemory workingMemory) {

        Object[] options = {"Yes",

                            "No"};

        int n = JOptionPane.showOptionDialog(frame,

            "Would you like to checkout?",

            "",

            JOptionPane.YES_NO_OPTION,

            JOptionPane.QUESTION_MESSAGE,

            null,

            options,

            options[0]);

        if (n == 0) {

            workingMemory.setFocus( "checkout" );

        }

    }

    function boolean requireTank(JFrame frame, WorkingMemory workingMemory, Order order, Product fishTank, int total) {

        Object[] options = {"Yes",

                            "No"};

        int n = JOptionPane.showOptionDialog(frame,

            "Would you like to buy a tank for your " + total + " fish?",

            "Purchase Suggestion",

            JOptionPane.YES_NO_OPTION,

            JOptionPane.QUESTION_MESSAGE,

            null,

            options,

            options[0]);

        System.out.print( "SUGGESTION: Would you like to buy a tank for your "

                          + total + " fish? - " );

        if (n == 0) {

            Purchase purchase = new Purchase( order, fishTank );

            workingMemory.insert( purchase );

            order.addItem( purchase );

            System.out.println( "Yes" );

        } else {

            System.out.println( "No" );

        }

        return true;

    }

Having these functions in the rules file just makes the Pet Store example more compact. In real life you probably have the functions in a file of their own, within the same rules package, or as a static method on a standard Java class, and import them, using `import function my.package.Foo.hello`.

The purpose of these two functions is:

- `doCheckout()` displays a dialog asking users whether they wish to checkout. If they do, focus is set to the `checkOut` agenda-group, allowing rules in that group to (potentially) fire.

- `requireTank()` displays a dialog asking users whether they wish to buy a tank. If so, a new fish tank `Product` is added to the order list in Working Memory.

We'll see the rules that call these functions later on. The next set of examples are from the Pet Store rules themselves. The first extract is the one that happens to fire first, partly because it has the `auto-focus` attribute set to true.

Example 7.56. Putting items into working memory: extract from PetStore.drl

    // Insert each item in the shopping cart into the Working Memory
    // Insert each item in the shopping cart into the Working Memory
    rule "Explode Cart"
        agenda-group "init"
        auto-focus true
        salience 10
        dialect "java"
    when
        $order : Order( grossTotal == -1 )
        $item : Purchase() from $order.items
    then
        insert( $item );
        kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "show items" ).setFocus();
        kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "evaluate" ).setFocus();
    end

This rule matches against all orders that do not yet have their `grossTotal` calculated . It loops for each purchase item in that order. Some parts of the "Explode Cart" rule should be familiar: the rule name, the salience (suggesting the order for the rules being fired) and the dialect set to `"java"`. There are three new features:

- `agenda-group` `"init"` defines the name of the agenda group. In this case, there is only one rule in the group. However, neither the Java code nor a rule consequence sets the focus to this group, and therefore it relies on the next attribute for its chance to fire.

- `auto-focus` `true` ensures that this rule, while being the only rule in the agenda group, gets a chance to fire when `fireAllRules()` is called from the Java code.

- `kcontext....setFocus()` sets the focus to the `"show items"` and `"evaluate"` agenda groups in turn, permitting their rules to fire. In practice, we loop through all items on the order, inserting them into memory, then firing the other rules after each insert.

The next two listings show the rules within the `"show items"` and `evaluate` agenda groups. We look at them in the order that they are called.

Example 7.57. Show Items in the GUI - extract from PetStore.drl

    rule "Show Items"
        agenda-group "show items"
        dialect "mvel"
    when
        $order : Order( )
        $p : Purchase( order == $order )
    then
       textArea.append( $p.product + "\n");
    end

The `"show items"` agenda-group has only one rule, called "Show Items" (note the difference in case). For each purchase on the order currently in the Working Memory (or Session), it logs details to the text area at the bottom of the GUI. The `textArea` variable used to do this is one of the global variables we looked at earlier.

The `evaluate` Agenda group also gains focus from the `"Explode Cart"` rule listed previously. This Agenda group has two rules, `"Free Fish Food Sample"` and `"Suggest Tank"`, shown below.

Example 7.58. Evaluate Agenda Group: extract from PetStore.drl

    // Free Fish Food sample when we buy a Gold Fish if we haven't already bought
    // Fish Food and don't already have a Fish Food Sample
    rule "Free Fish Food Sample"
        agenda-group "evaluate"
        dialect "mvel"
    when
        $order : Order()
        not ( $p : Product( name == "Fish Food") &amp;&amp; Purchase( product == $p ) )
        not ( $p : Product( name == "Fish Food Sample") &amp;&amp; Purchase( product == $p ) )
        exists ( $p : Product( name == "Gold Fish") &amp;&amp; Purchase( product == $p ) )
        $fishFoodSample : Product( name == "Fish Food Sample" );
    then
        System.out.println( "Adding free Fish Food Sample to cart" );
        purchase = new Purchase($order, $fishFoodSample);
        insert( purchase );
        $order.addItem( purchase );
    end

    // Suggest a tank if we have bought more than 5 gold fish and don't already have one
    rule "Suggest Tank"
        agenda-group "evaluate"
        dialect "java"
    when
        $order : Order()
        not ( $p : Product( name == "Fish Tank") &amp;&amp; Purchase( product == $p ) )
        ArrayList( $total : size &gt; 5 ) from collect( Purchase( product.name == "Gold Fish" ) )
        $fishTank : Product( name == "Fish Tank" )
    then
        requireTank(frame, drools.getWorkingMemory(), $order, $fishTank, $total);
    end

The rule `"Free Fish Food Sample"` will only fire if

- we don't already have any fish food, and

- we don't already have a free fish food sample, and

- we do have a Gold Fish in our order.

If the rule does fire, it creates a new product (Fish Food Sample), and adds it to the order in Working Memory.

The rule `"Suggest Tank"` will only fire if

- we don't already have a Fish Tank in our order, and

- we do have more than 5 Gold Fish Products in our order.

If the rule does fire, it calls the `requireTank()` function that we looked at earlier (showing a Dialog to the user, and adding a Tank to the order / working memory if confirmed). When calling the requireTank() function the rule passes the global frame variable so that the function has a handle to the Swing GUI.

The next rule we look at is `"do checkout"`.

Example 7.59. Doing the Checkout - extract (6) from PetStore.drl

    rule "do checkout"
        dialect "java"
        when
        then
            doCheckout(frame, drools.getWorkingMemory());
    end

The rule `"do checkout"` has no agenda group set and no auto-focus attribute. As such, is is deemed part of the default (MAIN) agenda group. This group gets focus by default when all the rules in agenda-groups that explicitly had focus set to them have run their course.

There is no LHS to the rule, so the RHS will always call the `doCheckout()` function. When calling the `doCheckout()` function, the rule passes the global `frame` variable to give the function a handle to the Swing GUI. As we saw earlier, the `doCheckout()` function shows a confirmation dialog to the user. If confirmed, the function sets the focus to the checkout agenda-group, allowing the next lot of rules to fire.

Example 7.60. Checkout Rules: extract from PetStore.drl

    rule "Gross Total"
        agenda-group "checkout"
        dialect "mvel"
    when
       $order : Order( grossTotal == -1)
       Number( total : doubleValue )
           from accumulate( Purchase( $price : product.price ), sum( $price ) )
    then
        modify( $order ) { grossTotal = total };
        textArea.append( "\ngross total=" + total + "\n" );
    end

    rule "Apply 5% Discount"
        agenda-group "checkout"
    dialect "mvel"
    when
       $order : Order( grossTotal &gt;= 10 &amp;&amp; &lt; 20 )
    then
       $order.discountedTotal = $order.grossTotal * 0.95;
       textArea.append( "discountedTotal total=" + $order.discountedTotal + "\n" );
    end

    rule "Apply 10% Discount"
        agenda-group "checkout"
        dialect "mvel"
    when
       $order : Order( grossTotal &gt;= 20 )
    then
       $order.discountedTotal = $order.grossTotal * 0.90;
       textArea.append( "discountedTotal total=" + $order.discountedTotal + "\n" );
    end

There are three rules in the checkout agenda-group:

- If we haven't already calculated the gross total, `Gross Total` accumulates the product prices into a total, puts this total into Working Memory, and displays it via the Swing `JTextArea`, using the `textArea` global variable yet again.

- If our gross total is between 10 and 20, `"Apply 5% Discount"` calculates the discounted total and adds it to the Working Memory and displays it in the text area.

- If our gross total is not less than 20, `"Apply 10% Discount"` calculates the discounted total and adds it to the Working Memory and displays it in the text area.

Now that we've run through what happens in the code, let's have a look at what happens when we actually run the code. The file `PetStore.java` contains a `main()` method, so that it can be run as a standard Java application, either from the command line or via the IDE. This assumes you have your classpath set correctly. (See the start of the examples section for more information.)

The first screen that we see is the Pet Store Demo. It has a list of available products (top left), an empty list of selected products (top right), checkout and reset buttons (middle) and an empty system messages area (bottom).

Figure 7.13. PetStore Demo just after Launch

To get to this point, the following things have happened:

1. The `main()` method has run and loaded the Rule Base but not yet fired the rules. So far, this is the only code in connection with rules that has been run.

2. A new `PetStoreUI` object has been created and given a handle to the Rule Base, for later use.

3. Various Swing components do their stuff, and the above screen is shown and waits for user input.

Clicking on various products from the list might give you a screen similar to the one below.

Figure 7.14. PetStore Demo with Products Selected

Note that no rules code has been fired here. This is only Swing code, listening for mouse click events, and adding some selected product to the `TableModel` object for display in the top right hand section. (As an aside, note that this is a classic use of the Model View Controller design pattern).

It is only when we press the "Checkout" button that we fire our business rules, in roughly the same order that we walked through the code earlier.

1. Method `CheckOutCallBack.checkout()` is called (eventually) by the Swing class waiting for the click on the "Checkout" button. This inserts the data from the `TableModel` object (top right hand side of the GUI), and inserts it into the Session's Working Memory. It then fires the rules.

2. The `"Explode Cart"` rule is the first to fire, given that it has `auto-focus` set to true. It loops through all the products in the cart, ensures that the products are in the Working Memory, and then gives the `"Show Items"` and `Evaluation` agenda groups a chance to fire. The rules in these groups add the contents of the cart to the text area (at the bottom of the window), decide whether or not to give us free fish food, and to ask us whether we want to buy a fish tank. This is shown in the figure below.

Figure 7.15. Do we want to buy a fish tank?

1. The Do Checkout rule is the next to fire as it (a) No other agenda group currently has focus and (b) it is part of the default (MAIN) agenda group. It always calls the doCheckout() function which displays a 'Would you like to Checkout?' Dialog Box.

2. The `doCheckout()` function sets the focus to the `checkout` agenda-group, giving the rules in that group the option to fire.

3. The rules in the the `checkout` agenda-group display the contents of the cart and apply the appropriate discount.

4. Swing then waits for user input to either checkout more products (and to cause the rules to fire again), or to close the GUI - see the figure below.

Figure 7.16. Petstore Demo after all rules have fired.

We could add more System.out calls to demonstrate this flow of events. The output, as it currently appears in the Console window, is given in the listing below.

Example 7.61. Console (System.out) from running the PetStore GUI

    Adding free Fish Food Sample to cart
    SUGGESTION: Would you like to buy a tank for your 6 fish? - Yes

## 7.8. Honest Politician Example

    Name: Honest Politician
    Main class: org.drools.examples.honestpolitician.HonestPoliticianExample
    Module: drools-examples
    Type: Java application
    Rules file: HonestPoliticianExample.drl
    Objective: Illustrate the concept of "truth maintenance" based on the logical insertion of facts

The Honest Politician example demonstrates truth maintenance with logical assertions. The basic premise is that an object can only exist while a statement is true. A rule's consequence can logically insert an object with the `insertLogical()` method. This means the object will only remain in the Working Memory as long as the rule that logically inserted it remains true. When the rule is no longer true the object is automatically retracted.

In this example there is the class `Politician`, with a name and a boolean value for being honest. Four politicians with honest state set to true are inserted.

Example 7.62. Class Politician

    public class Politician {

        private String name;

        private boolean honest;

        ...

    }

Example 7.63. Honest Politician: Execution

    Politician blair = new Politician("blair", true);

    Politician bush = new Politician("bush", true);

    Politician chirac = new Politician("chirac", true);

    Politician schroder = new Politician("schroder", true);

    ksession.insert( blair );

    ksession.insert( bush );

    ksession.insert( chirac );

    ksession.insert( schroder );

    ksession.fireAllRules();

The Console window output shows that, while there is at least one honest politician, democracy lives. However, as each politician is in turn corrupted by an evil corporation, so that all politicians become dishonest, democracy is dead.

Example 7.64. Honest Politician: Console Output

    Hurrah!!! Democracy Lives
    I'm an evil corporation and I have corrupted schroder
    I'm an evil corporation and I have corrupted chirac
    I'm an evil corporation and I have corrupted bush
    I'm an evil corporation and I have corrupted blair
    We are all Doomed!!! Democracy is Dead

As soon as there is at least one honest politician in the Working Memory a new `Hope` object is logically asserted. This object will only exist while there is at least one honest politician. As soon as all politicians are dishonest, the `Hope` object will be automatically retracted. This rule is given a salience of 10 to ensure that it fires before any other rule, as at this stage the "Hope is Dead" rule is actually true.

Example 7.65. Honest Politician: Rule "We have an honest politician"

    rule "We have an honest Politician"
        salience 10
        when
            exists( Politician( honest == true ) )
        then
            insertLogical( new Hope() );
    end

As soon as a `Hope` object exists the "Hope Lives" rule matches and fires. It has a salience of 10 so that it takes priority over "Corrupt the Honest".

Example 7.66. Honest Politician: Rule "Hope Lives"

    rule "Hope Lives"
        salience 10
            when
                exists( Hope() )
            then
                System.out.println("Hurrah!!! Democracy Lives");
    end

Now that there is hope and we have, at the start, four honest politicians, we have four activations for this rule, all in conflict. They will fire in turn, corrupting each politician so that they are no longer honest. When all four politicians have been corrupted we have no politicians with the property `honest == true`. Thus, the rule "We have an honest Politician" is no longer true and the object it logical inserted (due to the last execution of `new Hope()`) is automatically retracted.

Example 7.67. Honest Politician: Rule "Corrupt the Honest"

    rule "Corrupt the Honest"
        when
            politician : Politician( honest == true )
            exists( Hope() )
        then
            System.out.println( "I'm an evil corporation and I have corrupted " + politician.getName() );
            modify ( politician ) { honest = false };
    end

With the `Hope` object being automatically retracted, via the truth maintenance system, the conditional element `not` applied to `Hope` is no longer true so that the following rule will match and fire.

Example 7.68. Honest Politician: Rule "Hope is Dead"

    rule "Hope is Dead"
        when
            not( Hope() )
        then
            System.out.println( "We are all Doomed!!! Democracy is Dead" );
    end

Let's take a look at the Audit trail for this application:

Figure 7.17. Honest Politician Example Audit View

The moment we insert the first politician we have two activations. The rule "We have an honest Politician" is activated only once for the first inserted politician because it uses an `exists` conditional element, which matches once for any number. The rule "Hope is Dead" is also activated at this stage, because we have not yet inserted the `Hope` object. Rule "We have an honest Politician" fires first, as it has a higher salience than "Hope is Dead", which inserts the `Hope` object. (That action is highlighted green.) The insertion of the `Hope` object activates "Hope Lives" and de-activates "Hope is Dead"; it also activates "Corrupt the Honest" for each inserted honest politician. Rule "Hope Lives" executes, printing "Hurrah!!! Democracy Lives". Then, for each politician, rule "Corrupt the Honest" fires, printing "I'm an evil corporation and I have corrupted X", where X is the name of the politician, and modifies the politician's honest value to false. When the last honest politician is corrupted, `Hope` is automatically retracted, by the truth maintenance system, as shown by the blue highlighted area. The green highlighted area shows the origin of the currently selected blue highlighted area. Once the `Hope` fact is retracted, "Hope is dead" activates and fires printing "We are all Doomed!!! Democracy is Dead".

## 7.9. Sudoku Example

    Name: Sudoku
    Main class: org.drools.examples.sudoku.SudokuExample
    Type: Java application
    Rules file: sudoku.drl, validate.drl
    Objective: Demonstrates the solving of logic problems, and complex pattern matching.

This example demonstrates how Drools can be used to find a solution in a large potential solution space based on a number of constraints. We use the popular puzzle of Sudoku. This example also shows how Drools can be integrated into a graphical interface and how callbacks can be used to interact with a running Drools rules engine in order to update the graphical interface based on changes in the Working Memory at runtime.

### 7.9.1. Sudoku Overview

Sudoku is a logic-based number placement puzzle. The objective is to fill a 9x9 grid so that each column, each row, and each of the nine 3x3 zones contains the digits from 1 to 9, once, and only once.

The puzzle setter provides a partially completed grid and the puzzle solver's task is to complete the grid with these constraints.

The general strategy to solve the problem is to ensure that when you insert a new number it should be unique in its particular 3x3 zone, row and column.

See Wikipedia for a more detailed description.

### 7.9.2. Running the Example

Download and install drools-examples as described above and then execute `java org.drools.examples.DroolsExamplesApp` and click on "SudokuExample".

The window contains an empty grid, but the program comes with a number of grids stored internally which can be loaded and solved. Click on "File", then "Samples" and select "Simple" to load one of the examples. Note that all buttons are disabled until a grid is loaded.

Loading the "Simple" example fills the grid according to the puzzle's initial state.

Click on the "Solve" button and the Drools-based engine will fill out the remaining values, and the buttons are inactive once more.

Alternatively, you may click on the "Step" button to see the next digit found by the rule set. The Console window will display detailed information about the rules which are executing to solve the step in a human readable form. Some examples of these messages are presented below.

    single 8 at [0,1]
    column elimination due to [1,2]: remove 9 from [4,2]
    hidden single 9 at [1,2]
    row elimination due to [2,8]: remove 7 from [2,4]
    remove 6 from [3,8] due to naked pair at [3,2] and [3,7]
    hidden pair in row at [4,6] and [4,4]

Click on the "Dump" button to see the state of the grid, with cells showing either the established value or the remaining possibilitiescandidates.

           Col: 0     Col: 1     Col: 2     Col: 3     Col: 4     Col: 5     Col: 6     Col: 7     Col: 8
    Row 0:   2 4  7 9   2 456        4567 9   23 56  9  --- 5 ---  --- 1 ---    3  67 9  --- 8 ---     4 67
    Row 1:  12    7 9  --- 8 ---  1    67 9   23  6  9  --- 4 ---   23  67    1 3  67 9    3  67 9  --- 5 ---
    Row 2:  1  4  7 9  1  456     --- 3 ---      56 89      5 78       5678   --- 2 ---     4 67 9  1  4 67
    Row 3:  1234       12345      1  45      12  5  8   --- 6 ---   2  5 78       5 78      45 7    --- 9 ---
    Row 4:  --- 6 ---  --- 7 ---      5      --- 4 ---   2  5  8   --- 9 ---      5  8   --- 1 ---  --- 3 ---
    Row 5:  --- 8 ---  12 45      1  45   9  12  5      --- 3 ---   2  5 7        567       4567     2 4 67
    Row 6:  1 3   7    1 3  6     --- 2 ---    3 56 8       5  8     3 56 8   --- 4 ---    3 567 9  1    678
    Row 7:  --- 5 ---  1 34 6     1  4 678     3  6 8   --- 9 ---    34 6 8   1 3  678   --- 2 ---  1    678
    Row 8:    34       --- 9 ---     4 6 8   --- 7 ---  --- 1 ---   23456 8     3 56 8     3 56          6 8

Now, let us load a Sudoku grid that is deliberately invalid. Click on "File", "Samples" and "!DELIBERATELY BROKEN!". Note that this grid starts with some issues, for example the value 5 appears twice in the first row.

A few simple rules perform a sanity check, right after loading a grid. In this case, the following messages are printed on standard output:

    cell [0,8]: 5 has a duplicate in row 0
    cell [0,0]: 5 has a duplicate in row 0
    cell [6,0]: 8 has a duplicate in col 0
    cell [4,0]: 8 has a duplicate in col 0
    Validation complete.

Nevertheless, click on the "Solve" button to apply the solving rules to this invalid grid. This will not complete; some cells remain empty.

The solving functionality has been achieved by the use of rules that implement standard solving techniques. They are based on the sets of values that are still candidates for a cell. If, for instance, such a set contains a single value, then this is the value for the cell. A little less obvious is the single occurrence of a value in one of the groups of nine cells. The rules detecting these situations insert a fact of type Setting with the solution value for some specific cell. This fact causes the elimination of this value from all other cells in any of the groups the cell belongs to. Finally, it is retracted.

Other rules merely reduce the permissible values for some cells. Rules "naked pair", "hidden pair in row", "hidden pair in column" and "hidden pair in square" merely eliminate possibilities but do not establish solutions. More sophisticated eliminations are done by "X-wings in rows", "X-wings in columns", "intersection removal row" and "intersection removal column".

### 7.9.3. Java Source and Rules Overview

The Java source code can be found in the /src/main/java/org/drools/examples/sudoku directory, with the two DRL files defining the rules located in the /src/main/rules/org/drools/examples/sudoku directory.

The package `org.drools.examples.sudoku.swing` contains a set of classes which implement a framework for Sudoku puzzles. Note that this package does not have any dependencies on the Drools libraries. `SudokuGridModel` defines an interface which can be implemented to store a Sudoku puzzle as a 9x9 grid of `Cell` objects. `SudokuGridView` is a Swing component which can visualize any implementation of `SudokuGridModel`. `SudokuGridEvent` and `SudokuGridListener` are used to communicate state changes between the model and the view: events are fired when a cell's value is resolved or changed. If you are familiar with the model-view-controller patterns in other Swing components such as `JTable` then this pattern should be familiar. `SudokuGridSamples` provides a number of partially filled Sudoku puzzles for demonstration purposes.

Package `org.drools.examples.sudoku.rules` contains a utility class with a method for compiling DRL files.

The package `org.drools.examples.sudoku` contains a set of classes implementing the elementary `Cell` object and its various aggregations: the `CellFile` subtypes `CellRow` and `CellCol` as well as `CellSqr`, all of which are subtypes of `CellGroup`. It's interesting to note that `Cell` and `CellGroup` are subclasses of `SetOfNine`, which provides a property `free` with the type `Set<Integer>`. For a `Cell` it represents the individual candidate set; for a `CellGroup` the set is the union of all candidate sets of its cells, or, simply, the set of digits that still need to be allocated.

With 81 `Cell` and 27 `CellGroup` objects and the linkage provided by the `Cell` properties `cellRow`, `cellCol` and `cellSqr` and the `CellGroup` property `cells`, a list of `Cell` objects, it is possible to write rules that detect the specific situations that permit the allocation of a value to a cell or the elimination of a value from some candidate set.

An object of class `Setting` is used for triggering the operations that accompany the allocation of a value: its removal from the candidate sets of sibling cells and associated cell groups. Moreover, the presence of a `Setting` fact is used in all rules that should detect a new situation; this is to avoid reactions to inconsistent intermediary states.

An object of class `Stepping` is used in a low priority rule to execute an emergency halt when a "Step" does not terminate regularly. This indicates that the puzzle cannot be solved by the program.

The class `org.drools.examples.sudoku.SudokuExample` implements a Java application combining the components described.

### 7.9.4. Sudoku Validator Rules (validate.drl)

Validation rules detect duplicate numbers in cell groups. They are combined in an agenda group which enables us to activate them, explicitly, after loading a puzzle.

The three rules "duplicate in cell..." are very similar. The first pattern locates a cell with an allocated value. The second pattern pulls in any of the three cell groups the cell belongs to. The final pattern would find a cell (other than the first one) with the same value as the first cell and in the same row, column or square, respectively.

Rule "terminate group" fires last. It prints a message and calls halt.

### 7.9.5. Sudoku Solving Rules (sudoku.drl)

There are three types of rules in this file: one group handles the allocation of a number to a cell, another group detects feasible allocations, and the third group eliminates values from candidate sets.

Rules "set a value", "eliminate a value from Cell" and "retract setting" depend on the presence of a `Setting` object. The first rule handles the assignment to the cell and the operations for removing the value from the "free" sets of the cell's three groups. Also, it decrements a counter that, when zero, returns control to the Java application that has called `fireUntilHalt()`. The purpose of rule "eliminate a value from Cell" is to reduce the candidate lists of all cells that are related to the newly assigned cell. Finally, when all eliminations have been made, rule "retract setting" retracts the triggering `Setting` fact.

There are just two rules that detect a situation where an allocation of a number to a cell is possible. Rule "single" fires for a `Cell` with a candidate set containing a single number. Rule "hidden single" fires when there is no cell with a single candidate but when there is a cell containing a candidate but this candidate is absent from all other cells in one of the three groups the cell belongs to. Both rules create and insert a `Setting` fact.

Rules from the largest group of rules implement, singly or in groups of two or three, various solving techniques, as they are employed when solving Sudoku puzzles manually.

Rule "naked pair" detects identical candidate sets of size 2 in two cells of a group; these two values may be removed from all other candidate sets of that group.

A similar idea motivates the three rules "hidden pair in..."; here, the rules look for a subset of two numbers in exactly two cells of a group, with neither value occurring in any of the other cells of this group. This, then, means that all other candidates can be eliminated from the two cells harbouring the hidden pair.

A pair of rules deals with "X-wings" in rows and columns. When there are only two possible cells for a value in each of two different rows (or columns) and these candidates lie also in the same columns (or rows), then all other candidates for this value in the columns (or rows) can be eliminated. If you follow the pattern sequence in one of these rules, you will see how the conditions that are conveniently expressed by words such as "same" or "only" result in patterns with suitable constraints or prefixed with "not".

The rule pair "intersection removal..." is based on the restricted occurrence of some number within one square, either in a single row or in a single column. This means that this number must be in one of those two or three cells of the row or column; hence it can be removed from the candidate sets of all other cells of the group. The pattern establishes the restricted occurrence and then fires for each cell outside the square and within the same cell file.

These rules are sufficient for many but certainly not for all Sudoku puzzles. To solve very difficult grids, the rule set would need to be extended with more complex rules. (Ultimately, there are puzzles that cannot be solved except by trial and error.)

## 7.10. Number Guess

    Name: Number Guess
    Main class: org.drools.examples.numberguess.NumberGuessExample
    Module: droolsjbpm-integration-examples (Note: this is in a different download, the droolsjbpm-integration download.)
    Type: Java application
    Rules file: NumberGuess.drl
    Objective: Demonstrate use of Rule Flow to organise Rules

The "Number Guess" example shows the use of Rule Flow, a way of controlling the order in which rules are fired. It uses widely understood workflow diagrams for defining the order in which groups of rules will be executed.

Example 7.69. Creating the Number Guess RuleBase: NumberGuessExample.main() - part 1

    final KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    kbuilder.add( ResourceFactory.newClassPathResource( "NumberGuess.drl",

                                                        ShoppingExample.class ),

                  ResourceType.DRL );

    kbuilder.add( ResourceFactory.newClassPathResource( "NumberGuess.rf",

                                                        ShoppingExample.class ),

                  ResourceType.DRF );

    final KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();

    kbase.addKnowledgePackages( kbuilder.getKnowledgePackages() );

The creation of the package and the loading of the rules (using the `add()` method) is the same as the previous examples. There is an additional line to add the Rule Flow (`NumberGuess.rf`), which provides the option of specifying different rule flows for the same Knowledge Base. Otherwise, the Knowledge Base is created in the same manner as before.

Example 7.70. Starting the RuleFlow: NumberGuessExample.main() - part 2

    final StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

    KnowledgeRuntimeLogger logger =

      KnowledgeRuntimeLoggerFactory.newFileLogger(ksession, "log/numberguess");

    ksession.insert( new GameRules( 100, 5 ) );

    ksession.insert( new RandomNumber() );

    ksession.insert( new Game() );

    ksession.startProcess( "Number Guess" );

    ksession.fireAllRules();

    logger.close();

    ksession.dispose();

Once we have a Knowledge Base, we can use it to obtain a Stateful Session. Into our session we insert our facts, i.e., standard Java objects. (For simplicity, in this sample, these classes are all contained within our `NumberGuessExample.java` file. Class `GameRules` provides the maximum range and the number of guesses allowed. Class `RandomNumber` automatically generates a number between 0 and 100 and makes it available to our rules, by insertion via the `getValue()` method. Class `Game` keeps track of the guesses we have made before, and their number.

Note that before we call the standard `fireAllRules()` method, we also start the process that we loaded earlier, via the `startProcess()` method. We'll learn where to obtain the parameter we pass ("Number Guess", i.e., the identifier of the rule flow) when we talk about the rule flow file and the graphical Rule Flow Editor below.

Before we finish the discussion of our Java code, we note that in some real-life application we would examine the final state of the objects. (Here, we could retrieve the number of guesses, to add it to a high score table.) For this example we are content to ensure that the Working Memory session is cleared by calling the `dispose()` method.

Figure 7.18. RuleFlow for the NumberGuess Example

If you open the `NumberGuess.rf` file in the Drools IDE (provided you have the JBoss Rules extensions installed correctly in Eclipse) you should see the above diagram, similar to a standard flowchart. Its icons are similar (but not exactly the same) as in the JBoss jBPM workflow product. Should you wish to edit the diagram, a menu of available components should be available to the left of the diagram in the IDE, which is called the palette. This diagram is saved in XML, an (almost) human readable format, using XStream.

If it is not already open, ensure that the Properties View is visible in the IDE. It can be opened by clicking "Window", then "Show View" and "Other", where you can select the "Properties" view. If you do this before you select any item on the rule flow (or click on the blank space in the rule flow) you should be presented with the following set of properties.

Figure 7.19. Properties for the Number Guess Rule Flow

Keep an eye on the Properties View as we progress through the example's rule flow, as it presents valuable information. In this case, it provides us with the identification of the Rule Flow Process that we used in our earlier code snippet, when we called `session.startProcess()`.

In the "Number Guess" Rule Flow we encounter several node types, many of them identified by an icon.

- The Start node (white arrow in a green circle) and the End node (red box) mark beginning and end of the rule flow.

- A Rule Flow Group box (yellow, without an icon) represents a Rule Flow Groups defined in our rules (DRL) file that we will look at later. For example, when the flow reaches the Rule Flow Group "Too High", only those rules marked with an attribute of `ruleflow-group` `"Too High"` can potentially fire.

- Action nodes (yellow, cog-shaped icon) perform standard Java method calls. Most action nodes in this example call `System.out.println()`, indicating the program's progress to the user.

- Split and Join Nodes (blue ovals, no icon) such as "Guess Correct?" and "More guesses Join" mark places where the flow of control can split, according to various conditions, and rejoin, respectively

- Arrows indicate the flow between the various nodes.

The various nodes in combination with the rules make the Number Guess game work. For example, the "Guess" Rule Flow Group allows only the rule "Get user Guess" to fire, because only that rule has a matching attribute of `ruleflow-group` `"Guess"`.

Example 7.71. A Rule firing only at a specific point in the Rule Flow: NumberGuess.drl

    rule "Get user Guess"
        ruleflow-group "Guess"
        no-loop
        when
            $r : RandomNumber()
            rules : GameRules( allowed : allowedGuesses )
            game : Game( guessCount < allowed )
            not ( Guess() )
        then
            System.out.println( "You have " + ( rules.allowedGuesses - game.guessCount )
                                + " out of " + rules.allowedGuesses
                                + " guesses left.\nPlease enter your guess from 0 to "
                                + rules.maxRange );
            br = new BufferedReader( new InputStreamReader( System.in ) );
            i = br.readLine();
            modify ( game ) { guessCount = game.guessCount + 1 }
            insert( new Guess( i ) );
    end

The rest of this rule is fairly standard. The LHS section (after `when`) of the rule states that it will be activated for each `RandomNumber` object inserted into the Working Memory where `guessCount` is less than `allowedGuesses` from the `GameRules` object and where the user has not guessed the correct number.

The RHS section (or consequence, after `then`) prints a message to the user and then awaits user input from `System.in`. After obtaining this input (the `readLine()` method call blocks until the return key is pressed) it modifies the guess count and inserts the new guess, making both available to the Working Memory.

The rest of the rules file is fairly standard: the package declares the dialect as MVEL, and various Java classes are imported. In total, there are five rules in this file:

1. Get User Guess, the Rule we examined above.

2. A Rule to record the highest guess.

3. A Rule to record the lowest guess.

4. A Rule to inspect the guess and retract it from memory if incorrect.

5. A Rule that notifies the user that all guesses have been used up.

One point of integration between the standard Rules and the RuleFlow is via the `ruleflow-group` attribute on the rules, as discussed above. A second point of integration between the rules (.drl) file and the Rules Flow .rf files is that the Split Nodes (the blue ovals) can use values in the Working Memory (as updated by the rules) to decide which flow of action to take. To see how this works, click on the "Guess Correct Node"; then within the Properties View, open the Constraints Editor by clicking the button at the right that appears once you click on the "Constraints" property line. You should see something similar to the diagram below.

Figure 7.20. Edit Constraints for the "Guess Correct" Node

Click on the "Edit" button beside "To node Too High" and you'll see a dialog like the one below. The values in the "Textual Editor" window follow the standard rule format for the LHS and can refer to objects in Working Memory. The consequence (RHS) is that the flow of control follows this node (i.e., "To node Too High") if the LHS expression evaluates to true.

Figure 7.21. Constraint Editor for the "Guess Correct" Node: value too high

Since the file `NumberGuess.java` contains a `main()` method, it can be run as a standard Java application, either from the command line or via the IDE. A typical game might result in the interaction below. The numbers in bold are typed in by the user.

Example 7.72. Example Console output where the Number Guess Example beat the human!

    You have 5 out of 5 guesses left.
    Please enter your guess from 0 to 100
    50
    Your guess was too high
    You have 4 out of 5 guesses left.
    Please enter your guess from 0 to 100
    25
    Your guess was too low
    You have 3 out of 5 guesses left.
    Please enter your guess from 0 to 100
    37
    Your guess was too low
    You have 2 out of 5 guesses left.
    Please enter your guess from 0 to 100
    44
    Your guess was too low
    You have 1 out of 5 guesses left.
    Please enter your guess from 0 to 100
    47
    Your guess was too low
    You have no more guesses
    The correct guess was 48

A summary of what is happening in this sample is:

1. The `main()` method of `NumberGuessExample.java` loads a Rule Base, creates a Stateful Session and inserts `Game`, `GameRules` and `RandomNumber` (containing the target number) objects into it. The method also sets the process flow we are going to use, and fires all rules. Control passes to the Rule Flow.

2. File `NumberGuess.rf`, the Rule Flow, begins at the "Start" node.

3. Control passes (via the "More guesses" join node) to the Guess node.

4. At the Guess node, the appropriate Rule Flow Group ("Get user Guess") is enabled. In this case the Rule "Guess" (in the `NumberGuess.drl` file) is triggered. This rule displays a message to the user, takes the response, and puts it into Working Memory. Flow passes to the next Rule Flow Node.

5. At the next node, "Guess Correct", constraints inspect the current session and decide which path to take.

   If the guess in step 4 was too high or too low, flow proceeds along a path which has an action node with normal Java code printing a suitable message and a Rule Flow Group causing a highest guess or lowest guess rule to be triggered. Flow passes from these nodes to step 6.

   If the guess in step 4 was right, we proceed along the path towards the end of the Rule Flow. Before we get there, an action node with normal Java code prints a statement "you guessed correctly". There is a join node here (just before the Rule Flow end) so that our no-more-guesses path (step 7) can also terminate the Rule Flow.

6. Control passes as per the Rule Flow via a join node, a guess incorrect Rule Flow Group (triggering a rule to retract a guess from Working Memory) onto the "More guesses" decision node.

7. The "More guesses" decision node (on the right hand side of the rule flow) uses constraints, again looking at values that the rules have put into the working memory, to decide if we have more guesses and if so, goto step 3. If not, we proceed to the end of the rule flow, via a Rule Flow Group that triggers a rule stating "you have no more guesses".

8. The loop over steps 3 to 7 continues until the number is guessed correctly, or we run out of guesses.

## 7.11. Conway's Game Of Life

    Name: Conway's Game Of Life
    Main class: org.drools.examples.conway.ConwayAgendaGroupRun
                org.drools.examples.conway.ConwayRuleFlowGroupRun
    Module: droolsjbpm-integration-examples (Note: this is in a different download, the droolsjbpm-integration download.)
    Type: Java application
    Rules file: conway-ruleflow.drl conway-agendagroup.drl
    Objective: Demonstrates 'accumulate', 'collect' and 'from'

Conway's Game Of Life, described in http://en.wikipedia.org/wiki/Conway's_Game_of_Life and in http://www.math.com/students/wonders/life/life.html, is a famous cellular automaton conceived in the early 1970's by the mathematician John Conway. While the system is well known as "Conway's Game Of Life", it really isn't a game at all. Conway's system is more like a simulation of a form of life. Don't be intimidated. The system is terribly simple and terribly interesting. Math and Computer Science students alike have marvelled over Conway's system for more than 30 years now. The application presented here is a Swing-based implementation of Conway's Game of Life. The rules that govern the system are implemented as business rules using Drools. This document will explain the rules that drive the simulation and discuss the Drools parts of the implementation.

We'll first introduce the grid view, shown below, designed for the visualisation of the game, showing the "arena" where the life simulation takes place. Initially the grid is empty, meaning that there are no live cells in the system. Each cell is either alive or dead, with live cells showing a green ball. Preselected patterns of live cells can be chosen from the "Pattern" drop-down list. Alternatively, individual cells can be doubled-clicked to toggle them between live and dead. It's important to understand that each cell is related to its neighboring cells, which is fundamental for the game's rules. Neighbors include not only cells to the left, right, top and bottom but also cells that are connected diagonally, so that each cell has a total of 8 neighbors. Exceptions are the four corner cells which have only three neighbors, and the cells along the four border, with five neighbors each.

Figure 7.22. Conway's Game of Life: Starting a new game

So what are the basic rules that govern this game? Its goal is to show the development of a population, generation by generation. Each generation results from the preceding one, based on the simultaneous evaluation of all cells. This is the simple set of rules that govern what the next generation will look like:

- If a live cell has fewer than 2 live neighbors, it dies of loneliness.

- If a live cell has more than 3 live neighbors, it dies from overcrowding.

- If a dead cell has exactly 3 live neighbors, it comes to life.

That is all there is to it. Any cell that doesn't meet any of those criteria is left as is for the next generation. With those simple rules in mind, go back and play with the system a little bit more and step through some generations, one at a time, and notice these rules taking their effect.

The screenshot below shows an example generation, with a number of live cells. Don't worry about matching the exact patterns represented in the screen shot. Just get some groups of cells added to the grid. Once you have groups of live cells in the grid, or select a pre-designed pattern, click the "Next Generation" button and notice what happens. Some of the live cells are killed (the green ball disappears) and some dead cells come to life (a green ball appears). Step through several generations and see if you notice any patterns. If you click on the "Start" button, the system will evolve itself so you don't need to click the "Next Generation" button over and over. Play with the system a little and then come back here for more details of how the application works.

Figure 7.23. Conway's Game of Life: A running game

Now lets delve into the code. As this is an advanced example we'll assume that by now you know your way around the Drools framework and are able to connect the presented highlight, so that we'll just focus at a high level overview. The example has two ways to execute, one way uses Agenda Groups to manage execution flow, and the other one uses Rule Flow Groups to manage execution flow. These two versions are implemented in `ConwayAgendaGroupRun` and `ConwayRuleFlowGroupRun`, respectively. Here, we'll discuss the Rule Flow version, as it's what most people will use.

All the `Cell` objects are inserted into the Session and the rules in the `ruleflow-group` "register neighbor" are allowed to execute by the Rule Flow process. This group of four rules creates `Neighbor` relations between some cell and its northeastern, northern, northwestern and western neighbors. This relation is bidirectional, which takes care of the other four directions. Border cells don't need any special treatment - they simply won't be paired with neighboring cells where there isn't any. By the time all activations have fired for these rules, all cells are related to all their neighboring cells.

Example 7.73. Conway's Game of Life: Register Cell Neighbour relations

    rule "register north east"
        ruleflow-group "register neighbor"
    when
        $cell: Cell( $row : row, $col : col )
        $northEast : Cell( row  == ($row - 1), col == ( $col + 1 ) )
    then
        insert( new Neighbor( $cell, $northEast ) );
        insert( new Neighbor( $northEast, $cell ) );
    end

    rule "register north"
        ruleflow-group "register neighbor"
    when
        $cell: Cell( $row : row, $col : col )
        $north : Cell( row  == ($row - 1), col == $col )
    then
        insert( new Neighbor( $cell, $north ) );
        insert( new Neighbor( $north, $cell ) );
    end

    rule "register north west"
        ruleflow-group "register neighbor"
    when
        $cell: Cell( $row : row, $col : col )
        $northWest : Cell( row  == ($row - 1), col == ( $col - 1 ) )
    then
        insert( new Neighbor( $cell, $northWest ) );
        insert( new Neighbor( $northWest, $cell ) );
    end

    rule "register west"
        ruleflow-group "register neighbor"
    when
        $cell: Cell( $row : row, $col : col )
        $west : Cell( row  == $row, col == ( $col - 1 ) )
    then
        insert( new Neighbor( $cell, $west ) );
        insert( new Neighbor( $west, $cell ) );
    end

Once all the cells are inserted, some Java code applies the pattern to the grid, setting certain cells to Live. Then, when the user clicks "Start" or "Next Generation", it executes the "Generation" ruleflow. This ruleflow is responsible for the management of all changes of cells in each generation cycle.

Figure 7.24. Conway's Game of Life: rule flow "Generation"

The rule flow process first enters the "evaluate" group, which means that any active rule in the group can fire. The rules in this group apply the Game-of-Life rules discussed in the beginning of the example, determining the cells to be killed and the ones to be given life. We use the "phase" attribute to drive the reasoning of the Cell by specific groups of rules; typically the phase is tied to a Rule Flow Group in the Rule Flow process definition. Notice that it doesn't actually change the state of any `Cell` objectss at this point; this is because it's evaluating the grid in turn and it must complete the full evaluation until those changes can be applied. To achieve this, it sets the cell to a "phase" which is either `Phase.KILL` or `Phase.BIRTH`, used later to control actions applied to the `Cell` object.

Example 7.74. Conway's Game of Life: Evaluate Cells with state changes

    rule "Kill The Lonely"
        ruleflow-group "evaluate"
        no-loop
    when
    //  A live cell has fewer than 2 live neighbors
        theCell: Cell( liveNeighbors < 2, cellState == CellState.LIVE,
                       phase == Phase.EVALUATE )
    then
        modify( theCell ){
            setPhase( Phase.KILL );
        }
    end

    rule "Kill The Overcrowded"
        ruleflow-group "evaluate"
        no-loop
    when
    //  A live cell has more than 3 live neighbors
        theCell: Cell( liveNeighbors > 3, cellState == CellState.LIVE,
                       phase == Phase.EVALUATE )
    then
        modify( theCell ){
            setPhase( Phase.KILL );
        }
    end

    rule "Give Birth"
        ruleflow-group "evaluate"
        no-loop
    when
    //  A dead cell has 3 live neighbors
        theCell: Cell( liveNeighbors == 3, cellState == CellState.DEAD,
                       phase == Phase.EVALUATE )
    then
        modify( theCell ){
            theCell.setPhase( Phase.BIRTH );
        }
    end

Once all `Cell` objects in the grid have been evaluated, we first clear any calculation activations that occurred from any previous data changes. This is done via the "reset calculate" rule, which clears any activations in the "calculate" group. We then enter a split in the rule flow which allows any activations in both the "kill" and the "birth" group to fire. These rules are responsible for applying the state change.

Example 7.75. Conway's Game of Life: Apply the state changes

    rule "reset calculate"
        ruleflow-group "reset calculate"
    when
    then
        WorkingMemory wm = drools.getWorkingMemory();
        wm.clearRuleFlowGroup( "calculate" );
    end

    rule "kill"
        ruleflow-group "kill"
        no-loop
    when
        theCell: Cell( phase == Phase.KILL )
    then
        modify( theCell ){
            setCellState( CellState.DEAD ),
            setPhase( Phase.DONE );
        }
    end

    rule "birth"
        ruleflow-group "birth"
        no-loop
    when
        theCell: Cell( phase == Phase.BIRTH )
    then
        modify( theCell ){
            setCellState( CellState.LIVE ),
            setPhase( Phase.DONE );
        }
    end

At this stage, a number of `Cell` objects have been modified with the state changed to either `LIVE` or `DEAD`. Now we get to see the power of the `Neighbor` facts defining the cell relations. When a cell becomes live or dead, we use the `Neighbor` relation to iterate over all surrounding cells, increasing or decreasing the `liveNeighbor` count. Any cell that has its count changed is also set to to the `EVALUATE` phase, to make sure it is included in the reasoning during the evaluation stage of the Rule Flow Process. Notice that we don't have to do any iteration ourselves; simply by applying the relations in the rules we make the rule engine do all the hard work for us, with a minimal amount of code. Once the live count has been determined and set for all cells, the Rule Flow Process comes to and end. If the user has initially clicked the "Start" button, the engine will restart the rule flow; otherwise the user may request another generation.

Example 7.76. Conway's Game of Life: Evaluate cells with state changes

    rule "Calculate Live"
        ruleflow-group "calculate"
        lock-on-active
    when
        theCell: Cell( cellState == CellState.LIVE )
        Neighbor( cell == theCell, $neighbor : neighbor )
    then
        modify( $neighbor ){
            setLiveNeighbors( $neighbor.getLiveNeighbors() + 1 ),
            setPhase( Phase.EVALUATE );
        }
    end

    rule "Calculate Dead"
        ruleflow-group "calculate"
        lock-on-active
    when
        theCell: Cell( cellState == CellState.DEAD )
        Neighbor( cell == theCell, $neighbor : neighbor )
    then
        modify( $neighbor ){
            setLiveNeighbors( $neighbor.getLiveNeighbors() - 1 ),
            setPhase( Phase.EVALUATE );
        }
    end

## 7.12. Pong

A Conversion for the classic game Pong. Use the keys A, Z and K, M. The ball should get faster after each bounce.

    Name: Example Pong
    Main class: org.drools.games.pong.PongMain

Figure 7.25. Pong Screenshot

## 7.13. Adventures with Drools

Based on the Adventure in Prolog, over at the Amzi website, http://www.amzi.com/AdventureInProlog/, we started to work on a text adventure game for Drools. They are ideal as they can start off simple and build in complexity and size over time, they also demonstrate key aspects of declarative relational programming.

    Name: Example Text Adventure
    Main class: org.drools.games.adventure.TextAdventure

You can view the 8 minute demonstration and introduction for the example at http://downloads.jboss.org/drools/videos/text-adventures.swf

Figure 7.26. Pong Screenshot

## 7.14. Wumpus World

    Name: Example Wumpus World
    Main class: org.drools.games.wumpus.WumpusWorldMain

Wumpus World is an AI example covered in the book "Artificial Intelligence : A Modern Approach". When the game first starts all the cells are greyed out. As you walk around they become visible. The cave has pits, a wumpus and gold. When you are next to a pit you will feel a breeze, when you are next to the wumpus you will smell a stench and see glitter when next to gold. The sensor icons are shown above the move buttons. If you walk into a pit or the wumpus, you die. A more detailed overview of Wumpus World can be found at http://www.cis.temple.edu/~giorgio/cis587/readings/wumpus.shtml. A 20 minute video showing how the game is created and works is at http://www.youtube.com/watch?v=4CvjKqUOEzM.

Figure 7.27. Wumpus World

Figure 7.28. Cave Screenshot

Figure 7.29. Signals Screenshot

Figure 7.30. Smell Stench

Figure 7.31. Move Up, Wumpus Collision

## 7.15. Miss Manners and Benchmarking

    Name: Miss Manners
    Main class: org.drools.benchmark.manners.MannersBenchmark
    Module: drools-examples
    Type: Java application
    Rules file: manners.drl
    Objective: Advanced walkthrough on the Manners benchmark, covers Depth conflict resolution in depth.

### 7.15.1. Introduction

Miss Manners is throwing a party and, being a good host, she wants to arrange good seating. Her initial design arranges everyone in male-female pairs, but then she worries about people have things to talk about. What is a good host to do? She decides to note the hobby of each guest so she can then arrange guests not only pairing them according to alternating sex but also ensuring that a guest has someone with a common hobby, at least on one side.

Figure 7.32. Miss Manners' Guests

#### 7.15.1.1. BenchMarking

Five benchmarks were established in the 1991 paper "Effects of Database Size on Rule System Performance: Five Case Studies" by David Brant, Timothy Grose, Bernie Lofaso and Daniel P. Miranker:

- Manners uses a depth-first search approach to determine the seating arrangements alternating women and men and ensuring one common hobby for neighbors.

- Waltz establishes a three-dimensional interpretation of a line drawing by line labeling by constraint propagation.

- WaltzDB is a more general version of Waltz, supporting junctions of more than three lines and using a database.

- ARP is a route planner for a robotic air vehicle using the A* search algorithm to achieve minimal cost.

- Weaver VLSI router for channels and boxes using a black-board technique.

Manners has become the de facto rule engine benchmark. Its behavior, however, is now well known and many engines optimize for this, thus negating its usefulness as a benchmark which is why Waltz is becoming more favorable. These five benchmarks are also published at the University of Texas http://www.cs.utexas.edu/ftp/pub/ops5-benchmark-suite/.

#### 7.15.1.2. Miss Manners Execution Flow

After the first seating arrangement has been assigned, a depth-first recursion occurs which repeatedly assigns correct seating arrangements until the last seat is assigned. Manners uses a `Context` instance to control execution flow. The activity diagram is partitioned to show the relation of the rule execution to the current `Context` state.

Figure 7.33. Manners Activity Diagram

#### 7.15.1.3. The Data and Results

Before going deeper into the rules, let's first take a look at the asserted data and the resulting seating arrangement. The data is a simple set of five guests who should be arranged so that sexes alternate and neighbors have a common hobby.

The Data

The data is given in OPS5 syntax, with a parenthesized list of name and value pairs for each attribute. Each person has only one hobby.

(guest (name n1) (sex m) (hobby h1) )
(guest (name n2) (sex f) (hobby h1) )
(guest (name n2) (sex f) (hobby h3) )
(guest (name n3) (sex m) (hobby h3) )
(guest (name n4) (sex m) (hobby h1) )
(guest (name n4) (sex f) (hobby h2) )
(guest (name n4) (sex f) (hobby h3) )
(guest (name n5) (sex f) (hobby h2) )
(guest (name n5) (sex f) (hobby h1) )
(last_seat (seat 5) )

The Results

Each line of the results list is printed per execution of the "Assign Seat" rule. They key bit to notice is that each line has a "pid" value one greater than the last. (The significance of this will be explained in the discussion of the rule "Assign Seating".) The "ls", "rs", "ln" and "rn" refer to the left and right seat and neighbor's name, respectively. The actual implementation uses longer attribute names (e.g., `leftGuestName`, but here we'll stick to the notation from the original implementation.

[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[Seating id=2, pid=1, done=false, ls=1, ln=n5, rs=2, rn=n4]
[Seating id=3, pid=2, done=false, ls=2, ln=n4, rs=3, rn=n3]
[Seating id=4, pid=3, done=false, ls=3, rn=n3, rs=4, rn=n2]
[Seating id=5, pid=4, done=false, ls=4, ln=n2, rs=5, rn=n1]

### 7.15.2. In depth Discussion

#### 7.15.2.1. Cheating

Manners has been designed to exercise cross product joins and Agenda activities. Many people not understanding this tweak the example to achieve better performance, making their port of the Manners benchmark pointless. Known cheats or porting errors for Miss Manners are:

- Using arrays for a guests hobbies, instead of asserting each one as a single fact massively reduces the cross products.

- Altering the sequence of data can also reduce the amount of matching, increasing execution speed.

- It's possible to change the `not` Conditional Element so that the test algorithm only uses the "first-best-match", which is, basically, transforming the test algorithm to backward chaining. The results are only comparable to other backward chaining rule engines or ports of Manners.

- Removing the context so the rule engine matches the guests and seats prematurely. A proper port will prevent facts from matching using the context start.

- It's possible to prevent the rule engine from performing combinatorial pattern matching.

- If no facts are retracted in the reasoning cycle, as a result of the `not` CE, the port is incorrect.

#### 7.15.2.2. Conflict Resolution

The Manners benchmark was written for OPS5 which has two conflict resolution strategies, LEX and MEA. LEX is a chain of several strategies including salience, recency and complexity. The recency part of the strategy drives the depth first (LIFO) firing order. The CLIPS manual documents the Recency strategy as follows:

| Every fact and instance is marked internally with a "time tag" to indicate its relative recency with respect to every other fact and instance in the system. The pattern entities associated with each rule activation are sorted in descending order for determining placement. An activation with a more recent pattern entity is placed before activations with less recent pattern entities. To determine the placement order of two activations, compare the sorted time tags of the two activations one by one starting with the largest time tags. The comparison should continue until one activation’s time tag is greater than the other activation’s corresponding time tag. The activation with the greater time tag is placed before the other activation on the agenda. If one activation has more pattern entities than the other activation and the compared time tags are all identical, then the activation with more time tags is placed before the other activation on the agenda. |
| --CLIPS Reference Manual

However Jess and CLIPS both use the Depth strategy, which is simpler and lighter, which Drools also adopted. The CLIPS manual documents the Depth strategy as:

| Newly activated rules are placed above all rules of the same salience. For example, given that fact-a activates rule-1 and rule-2 and fact-b activates rule-3 and rule-4, then if fact-a is asserted before fact-b, rule-3 and rule-4 will be above rule-1 and rule-2 on the agenda. However, the position of rule-1 relative to rule-2 and rule-3 relative to rule-4 will be arbitrary. |
| --CLIPS Reference Manual

The initial Drools implementation for the Depth strategy would not work for Manners without the use of salience on the "make_path" rule. The CLIPS support team had this to say:

| The default conflict resolution strategy for CLIPS, Depth, is different than the default conflict resolution strategy used by OPS5. Therefore if you directly translate an OPS5 program to CLIPS, but use the default depth conflict resolution strategy, you're only likely to get the correct behavior by coincidence. The LEX and MEA conflict resolution strategies are provided in CLIPS to allow you to quickly convert and correctly run an OPS5 program in CLIPS. |
| --Clips Support Forum

Investigation into the CLIPS code reveals there is undocumented functionality in the Depth strategy. There is an accumulated time tag used in this strategy; it's not an extensively fact by fact comparison as in the recency strategy, it simply adds the total of all the time tags for each activation and compares.

#### 7.15.2.3. Rule "assignFirstSeat"

Once the context is changed to `START_UP`, activations are created for all asserted guest. Because all activations are created as the result of a single Working Memory action, they all have the same Activation time tag. The last asserted `Guest` object would have a higher fact time tag, and its Activation would fire because it has the highest accumulated fact time tag. The execution order in this rule has little importance, but has a big impact in the rule "Assign Seat". The activation fires and asserts the first `Seating` arrangement and a `Path`, and then sets the `Context` attribute `state` to create an activation for rule `findSeating`.

    rule assignFirstSeat
        when
            context : Context( state == Context.START_UP )
            guest : Guest()
            count : Count()
        then
            String guestName = guest.getName();

            Seating seating =
              new Seating( count.getValue(), 1, true, 1, guestName, 1, guestName);
            insert( seating );

            Path path = new Path( count.getValue(), 1, guestName );
            insert( path );

            modify( count ) { setValue ( count.getValue() + 1 )  }

        System.out.println( "assign first seat :  " + seating + " : " + path );

            modify( context ) {
                setState( Context.ASSIGN_SEATS )
            }
    end

#### 7.15.2.4. Rule "findSeating"

This rule determines each of the `Seating` arrangements. The rule creates cross product solutions for all asserted `Seating` arrangements against all the asserted guests except against itself or any already assigned chosen solutions.

    rule findSeating
       when
           context : Context( state == Context.ASSIGN_SEATS )
           $s      : Seating( pathDone == true )
           $g1     : Guest( name == $s.rightGuestName )
           $g2     : Guest( sex != $g1.sex, hobby == $g1.hobby )

           count   : Count()

           not ( Path( id == $s.id, guestName == $g2.name) )
           not ( Chosen( id == $s.id, guestName == $g2.name, hobby == $g1.hobby) )
       then
           int rightSeat = $s.getRightSeat();
           int seatId = $s.getId();
           int countValue = count.getValue();

           Seating seating =
             new Seating( countValue, seatId, false, rightSeat,
                          $s.getRightGuestName(), rightSeat + 1, $g2.getName() );
           insert( seating );

           Path path = new Path( countValue, rightSeat + 1, $g2.getName()  );
           insert( path );

           Chosen chosen = new Chosen( seatId, $g2.getName(), $g1.getHobby() );
           insert( chosen  );

           System.err.println( "find seating : " + seating + " : " + path +
                               " : " + chosen);

           modify( count ) {setValue(  countValue + 1 )}
           modify( context ) {setState( Context.MAKE_PATH )}
    end

However, as can be seen from the printed results shown earlier, it is essential that only the `Seating` with the highest `pid` cross product be chosen. How can this be possible if we have activations, of the same time tag, for nearly all existing `Seating` and `Guest` objects? For example, on the third iteration of `findSeating` the produced activations will be as shown below. Remember, this is from a very small data set, and with larger data sets there would be many more possible activated `Seating` solutions, with multiple solutions per `pid`:

=>[ActivationCreated(35): rule=findSeating
[fid:19:33]:[Seating id=3, pid=2, done=true, ls=2, ln=n4, rs=3, rn=n3]
[fid:4:4]:[Guest name=n3, sex=m, hobbies=h3]
[fid:3:3]:[Guest name=n2, sex=f, hobbies=h3]
=>[ActivationCreated(35): rule=findSeating
[fid:15:23]:[Seating id=2, pid=1, done=true, ls=1, ln=n5, rs=2, rn=n4]
[fid:5:5]:[Guest name=n4, sex=m, hobbies=h1]
[fid:2:2]:[Guest name=n2, sex=f, hobbies=h1]
=>[ActivationCreated(35): rule=findSeating
[fid:13:13]:[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[fid:9:9]:[Guest name=n5, sex=f, hobbies=h1]
[fid:1:1]:[Guest name=n1, sex=m, hobbies=h1]

The creation of all these redundant activations might seem pointless, but it must be remembered that Manners is not about good rule design; it's purposefully designed as a bad ruleset to fully stress-test the cross product matching process and the Agenda, which this clearly does. Notice that each activation has the same time tag of 35, as they were all activated by the change in the `Context` object to `ASSIGN_SEATS`. With OPS5 and LEX it would correctly fire the activation with the `Seating` asserted last. With Depth, the accumulated fact time tag ensures that the activation with the last asserted `Seating` fires.

#### 7.15.2.5. Rules "makePath" and "pathDone"

Rule `makePath` must always fire before `pathDone`. A `Path` object is asserted for each `Seating` arrangement, up to the last asserted `Seating`. Notice that the conditions in `pathDone` are a subset of those in `makePath` - so how do we ensure that `makePath` fires first?

    rule makePath
        when
            Context( state == Context.MAKE_PATH )
            Seating( seatingId:id, seatingPid:pid, pathDone == false )
            Path( id == seatingPid, pathGuestName:guestName, pathSeat:seat )
            not Path( id == seatingId, guestName == pathGuestName )
        then
            insert( new Path( seatingId, pathSeat, pathGuestName ) );
    end

    rule pathDone
        when
            context : Context( state == Context.MAKE_PATH )
            seating : Seating( pathDone == false )
        then
            modify( seating ) {setPathDone( true )}

        modify( context ) {setState( Context.CHECK_DONE)}
    end

Figure 7.34. Rete Diagram

Both rules end up on the Agenda in conflict and with identical activation time tags. However, the accumulate fact time tag is greater for "Make Path" so it gets priority.

#### 7.15.2.6. Rules "continue" and "areWeDone"

Rule `areWeDone` only activates when the last seat is assigned, at which point both rules will be activated. For the same reason that `makePath` always wins over `path Done`, `areWeDone` will take priority over rule `continue`.

    rule areWeDone
        when
            context : Context( state == Context.CHECK_DONE )
            LastSeat( lastSeat: seat )
            Seating( rightSeat == lastSeat )
        then
            modify( context ) {setState(Context.PRINT_RESULTS )}
    end

    rule continue
        when
            context : Context( state == Context.CHECK_DONE )
        then
            modify( context ) {setState( Context.ASSIGN_SEATS )}
    end

### 7.15.3. Output Summary

Assign First seat
=>[fid:13:13]:[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
=>[fid:14:14]:[Path id=1, seat=1, guest=n5]
==>[ActivationCreated(16): rule=findSeating
[fid:13:13]:[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[fid:9:9]:[Guest name=n5, sex=f, hobbies=h1]
[fid:1:1]:[Guest name=n1, sex=m, hobbies=h1]
==>[ActivationCreated(16): rule=findSeating
[fid:13:13]:[Seating id=1 , pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[fid:9:9]:[Guest name=n5, sex=f, hobbies=h1]
[fid:5:5]:[Guest name=n4, sex=m, hobbies=h1]*
Assign Seating
=>[fid:15:17] :[Seating id=2 , pid=1 , done=false, ls=1, lg=n5, rs=2, rn=n4]
=>[fid:16:18]:[Path id=2, seat=2, guest=n4]
=>[fid:17:19]:[Chosen id=1, name=n4, hobbies=h1]
=>[ActivationCreated(21): rule=makePath
[fid:15:17] : [Seating id=2, pid=1, done=false, ls=1, ln=n5, rs=2, rn=n4]
[fid:14:14] : [Path id=1, seat=1, guest=n5]*
==>[ActivationCreated(21): rule=pathDone
[Seating id=2, pid=1, done=false, ls=1, ln=n5, rs=2, rn=n4]*
Make Path
=>[fid:18:22:[Path id=2, seat=1, guest=n5]]
Path Done
Continue Process
=>[ActivationCreated(25): rule=findSeating
[fid:15:23]:[Seating id=2, pid=1, done=true, ls=1, ln=n5, rs=2, rn=n4]
[fid:7:7]:[Guest name=n4, sex=f, hobbies=h3]
[fid:4:4] : [Guest name=n3, sex=m, hobbies=h3]*
=>[ActivationCreated(25): rule=findSeating
[fid:15:23]:[Seating id=2, pid=1, done=true, ls=1, ln=n5, rs=2, rn=n4]
[fid:5:5]:[Guest name=n4, sex=m, hobbies=h1]
[fid:2:2]:[Guest name=n2, sex=f, hobbies=h1], [fid:12:20] : [Count value=3]
=>[ActivationCreated(25): rule=findSeating
[fid:13:13]:[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[fid:9:9]:[Guest name=n5, sex=f, hobbies=h1]
[fid:1:1]:[Guest name=n1, sex=m, hobbies=h1]
Assign Seating
=>[fid:19:26]:[Seating id=3, pid=2, done=false, ls=2, lnn4, rs=3, rn=n3]]
=>[fid:20:27]:[Path id=3, seat=3, guest=n3]]
=>[fid:21:28]:[Chosen id=2, name=n3, hobbies=h3}]
=>[ActivationCreated(30): rule=makePath
[fid:19:26]:[Seating id=3, pid=2, done=false, ls=2, ln=n4, rs=3, rn=n3]
[fid:18:22]:[Path id=2, seat=1, guest=n5]*
=>[ActivationCreated(30): rule=makePath
[fid:19:26]:[Seating id=3, pid=2, done=false, ls=2, ln=n4, rs=3, rn=n3]
[fid:16:18]:[Path id=2, seat=2, guest=n4]*
=>[ActivationCreated(30): rule=done
[fid:19:26]:[Seating id=3, pid=2, done=false, ls=2, ln=n4, rs=3, rn=n3]*
Make Path
=>[fid:22:31]:[Path id=3, seat=1, guest=n5]
Make Path
=>[fid:23:32] [Path id=3, seat=2, guest=n4]
Path Done
Continue Processing
=>[ActivationCreated(35): rule=findSeating
[fid:19:33]:[Seating id=3, pid=2, done=true, ls=2, ln=n4, rs=3, rn=n3]
[fid:4:4]:[Guest name=n3, sex=m, hobbies=h3]
[fid:3:3]:[Guest name=n2, sex=f, hobbies=h3], [fid:12:29]*
=>[ActivationCreated(35): rule=findSeating
[fid:15:23]:[Seating id=2, pid=1, done=true, ls=1, ln=n5, rs=2, rn=n4]
[fid:5:5]:[Guest name=n4, sex=m, hobbies=h1]
[fid:2:2]:[Guest name=n2, sex=f, hobbies=h1]
=>[ActivationCreated(35): rule=findSeating
[fid:13:13]:[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[fid:9:9]:[Guest name=n5, sex=f, hobbies=h1], [fid:1:1] : [Guest name=n1, sex=m, hobbies=h1]
Assign Seating
=>[fid:24:36]:[Seating id=4, pid=3, done=false, ls=3, ln=n3, rs=4, rn=n2]]
=>[fid:25:37]:[Path id=4, seat=4, guest=n2]]
=>[fid:26:38]:[Chosen id=3, name=n2, hobbies=h3]
==>[ActivationCreated(40): rule=makePath
[fid:24:36]:[Seating id=4, pid=3, done=false, ls=3, ln=n3, rs=4, rn=n2]
[fid:23:32]:[Path id=3, seat=2, guest=n4]*
==>[ActivationCreated(40): rule=makePath
[fid:24:36]:[Seating id=4, pid=3, done=false, ls=3, ln=n3, rs=4, rn=n2]
[fid:20:27]:[Path id=3, seat=3, guest=n3]*
=>[ActivationCreated(40): rule=makePath
[fid:24:36]:[Seating id=4, pid=3, done=false, ls=3, ln=n3, rs=4, rn=n2]
[fid:22:31]:[Path id=3, seat=1, guest=n5]*
=>[ActivationCreated(40): rule=done
[fid:24:36]:[Seating id=4, pid=3, done=false, ls=3, ln=n3, rs=4, rn=n2]*
Make Path
=>fid:27:41:[Path id=4, seat=2, guest=n4]
Make Path
=>fid:28:42]:[Path id=4, seat=1, guest=n5]]
Make Path
=>fid:29:43]:[Path id=4, seat=3, guest=n3]]
Path Done
Continue Processing
=>[ActivationCreated(46): rule=findSeating
[fid:15:23]:[Seating id=2, pid=1, done=true, ls=1, ln=n5, rs=2, rn=n4]
[fid:5:5]:[Guest name=n4, sex=m, hobbies=h1], [fid:2:2]
[Guest name=n2, sex=f, hobbies=h1]
=>[ActivationCreated(46): rule=findSeating
[fid:24:44]:[Seating id=4, pid=3, done=true, ls=3, ln=n3, rs=4, rn=n2]
[fid:2:2]:[Guest name=n2, sex=f, hobbies=h1]
[fid:1:1]:[Guest name=n1, sex=m, hobbies=h1]*
=>[ActivationCreated(46): rule=findSeating
[fid:13:13]:[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[fid:9:9]:[Guest name=n5, sex=f, hobbies=h1]
[fid:1:1]:[Guest name=n1, sex=m, hobbies=h1]
Assign Seating
=>[fid:30:47]:[Seating id=5, pid=4, done=false, ls=4, ln=n2, rs=5, rn=n1]
=>[fid:31:48]:[Path id=5, seat=5, guest=n1]
=>[fid:32:49]:[Chosen id=4, name=n1, hobbies=h1]

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