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[Jboss-svn-commits] JBL Code SVN: r4531 - in labs/jbossrules/trunk/documentation/manual/en: . Chapter-Examples Chapter-Rule_Engine Chapter-Rule_Language
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From: <jbo...@li...> - 2006-05-31 19:33:14
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Author: mar...@jb... Date: 2006-05-31 15:32:10 -0400 (Wed, 31 May 2006) New Revision: 4531 Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Agenda.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/AgendaEventListener.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/AgendaFilter.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Authoring.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Authoring.svg labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Copy of Section-What_is_a_Rule_Engine.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Inference_Engine.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Inference_Engine.svg labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Logical_Assertion.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Logical_Assertion.svg labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/PackageBuilder.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/PackageBuilderConfiguration.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/RuleBase.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/RuleBaseConfiguration.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/RuleBaseFactory.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Runtime.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Runtime.svg labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Section-The_Drools_Rule_Engine.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Stated_Assertion.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Stated_Assertion.svg labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Truth_Maintenance_System.drl.vsd labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Two_Phase.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Two_Phase.svg labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Two_Phase.vsd labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/WorkingMemory.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/WorkingMemoryEventListener.png labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/arch_overview.vsd Removed: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Section-Agenda.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Section-Event_Model.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Section-Rule_Base.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Section-Working_Memory.xml Modified: labs/jbossrules/trunk/documentation/manual/en/Chapter-Examples/Section-Examples.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Section-Rules.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Section-What_is_a_Rule_Engine.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Language/Section-Overview.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Language/Section-Package.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Language/Section-Query.xml labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Language/Section-Rule.xml labs/jbossrules/trunk/documentation/manual/en/master.xml Log: -updated docs Modified: labs/jbossrules/trunk/documentation/manual/en/Chapter-Examples/Section-Examples.xml =================================================================== --- labs/jbossrules/trunk/documentation/manual/en/Chapter-Examples/Section-Examples.xml 2006-05-31 19:25:18 UTC (rev 4530) +++ labs/jbossrules/trunk/documentation/manual/en/Chapter-Examples/Section-Examples.xml 2006-05-31 19:32:10 UTC (rev 4531) @@ -3,16 +3,20 @@ <title>Getting the examples</title> <para>Some examples are included as "integration tests" in the Drools source - code. On top of these, you can download the drools-examples module. This - module is always having more samples added to it by users. The example - project in eclipse requires that you have the plugin instilled: load up the - drools-examples project (it has an eclipse project already setup). 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 setup the - dependencies by hand of course.</para> + code - specifically the drools-compiler module. On top of these, you can + download the drools-examples module; which is a self contained Eclipse + project. This module is always having more samples added to it by users. The + example project in eclipse requires that you have the plugin instilled: load + up the drools-examples project (it has an eclipse project already setup). + 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 + setup the dependencies by hand of course.</para> <para>The examples can be found at http://anonsvn.labs.jboss.com/labs/jbossrules/trunk/drools-examples/ (you can browse online, or use a subversion client and download the project into Eclipse</para> + + <para>Future documentation will include walk throughs for each of the + examples.</para> </section> \ No newline at end of file Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Agenda.png =================================================================== (Binary files differ) Property changes on: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Agenda.png ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/AgendaEventListener.png =================================================================== (Binary files differ) Property changes on: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/AgendaEventListener.png ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/AgendaFilter.png =================================================================== (Binary files differ) Property changes on: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/AgendaFilter.png ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Authoring.png =================================================================== (Binary files differ) Property changes on: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Authoring.png 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labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Copy of Section-What_is_a_Rule_Engine.xml 2006-05-31 19:32:10 UTC (rev 4531) @@ -0,0 +1,576 @@ +<?xml version="1.0" encoding="UTF-8"?> +<section> + <title>What is a Rule Engine</title> + + <section> + <title>Background</title> + + <para>Artificial Intelligence (A.I.) is a very broad research area that + focuses on "Making computers think like people" and includes disciplines + like Neural Networks, Genetic Algorithms, Decision Trees, Frame Systems + and Expert Systems. Knowledge representation is the area of A.I. concerned + with how knowledge is represented and manipulated. Expert Systems use + Knowledge representation to facilitate the codification of knowledge into + a knowledge base which can be used for reasoning - i.e. we can process + data with this knowledge base to infer conclusions. Expert Systems are + also known as Knowledge-based Systems and Knowledge-based Expert System + and are considered 'applied artificial intelligence'; the process of + developing with an Expert System is Knowledge Engineering. Drools is a + Rule Engine that uses the Rule Based approached to implement an Expert + System and is more correctly classified as a Production Rules + System.</para> + + <para>The brain of a Rule Based approach is the Inference Engine; the role + of the Inference Engine is to infer conclusions about facts, from the + rules. </para> + + <figure> + <title>Inference Engine</title> + + <mediaobject> + <imageobject> + <imagedata align="center" fileref="Rule_Engine.svg" format="SVG" /> + </imageobject> + + <imageobject> + <imagedata align="center" fileref="Inference_Engine.png" + format="PNG" /> + </imageobject> + </mediaobject> + </figure> + + <para>A Production Rule is a two-part structure using logic to express one + or more conditions which when met result in execution of actions.</para> + + <programlisting>if + <conditions> +then + <actions></programlisting> + + <para>This is achieved using First Order Logic, or predicate logic, which + extends Propositional Logic. <ulink + url="http://en.wikipedia.org/wiki/Emil_Leon_Post">Emil Leon Post</ulink> + was the first to develop an inference based system using symbols to + express logic - as a consequence of this he was able to prove that any + logical system (including mathematics) could be expressed with such a + system.</para> + + <para>A proposition is a statement that can be classified as true or + false. If the truth can be determined from statement alone it is said to + be a "closed statement". In programming terms this is an expression that + does not reference any variables:</para> + + <para>10 = = 2 * 5</para> + + <para>Expressions that evaluate against one or more variables, facts, are + "open statements", in that we cannot determine whether the statement is + true until we have a variable instance to evaluate against:</para> + + <para>Person.sex == "male"</para> + + <para>With SQL if we look at the conclusion's action as simply returning + the matched fact to the user:</para> + + <para>select * from People where People.sex == "male"</para> + + <para>For any rows, which represent our facts, that are returned we have + inferred that those facts are male people. The following diagram shows how + the above SQL statement and People table can be represented in terms of + the previous Inference Engine diagram.</para> + + <figure> + <title>SQL as a simplistic Inference Engine</title> + + <mediaobject> + <imageobject> + <imagedata align="center" fileref="Male_People.svg" format="SVG" /> + </imageobject> + + <imageobject> + <imagedata align="center" fileref="Male_People.png" format="PNG" /> + </imageobject> + </mediaobject> + </figure> + + <para>So in java we can say that a simple proposition is of the form + 'variable' 'operator' 'value' - where we often refer to 'value' as being a + literal value - a proposition can be thought as a field constraint. + Further to this propositions can be combined with conjuntive and + disjuntive connectives, which is the logic theorists way of saying + '&&' and '||'. The following shows two open propositional + statements connected together with a single disjunctive connective.</para> + + <programlisting>person.getEyeColor().equals("blue") || person.getEyeColor().equals("green") </programlisting> + + <para>However Propositional Logic is not Turing complete in that you are + limited to the problems you can define because it cannot express criteria + for the structure of data. First Order Logic (FOL), or Predicate Logic, + extends Propositional Logic with two new quantifier concepts to allow + expressions defining structure - specifically universal and existential + quantifiers. Universal quantifiers allow you to check that something is + true for everything. Existential quantifiers check for the existence of + something, in that it occurs at least once.</para> + + <para>Imagine we have two classes - Student and Module. Module represents + each of the courses the Student attended for that semester, referenced by + the List collection. At the end of the semester each Module has a score. + If the Student has a Module score below 40 then they will fail that + semester - the existential quantifier can be used used with the "less than + 40" open proposition to check for the existence of a Module that is true + for the specified criteria.</para> + + <programlisting>public class Student { + private String name; + private List modules; + + ... +}</programlisting> + + <programlisting>public Class Module { + private String name; + private String studentName; + private int score;</programlisting> + + <para>Java is Turing complete in that you can write code, among other + things, to iterate data structures to check for existence. The following + should return a List of students who have failed the semester.</para> + + <programlisting>List failedStudents = new ArrayList(); + +for ( Iterator studentIter = students.iterator(); studentIter.hasNext() { + Student student = ( Student ) studentIter.next(); + for ( Iterator it = student.getModules.iterator(); it.hasNext(); ) { + Module module = ( Module ) it.next(); + if ( module.getScore() < 40 ) { + failedStudents.add( student ) ; + break; + } + } +}</programlisting> + + <para>Early SQL implementations where not Turing complete as they did not + provide quantifiers to asses the structure of data. However modern SQL + engines allow nesting of SQL which can be combined with keywords like + 'exists' and 'in': The following query would return a set of Students who + have failed the semester.</para> + + <programlisting>select + * +from + Students s +where exists ( + select + * + from + Modules m + where + m.student_name = s.name and + m.score < 40 +)</programlisting> + + <para>So modern SQL engines confuse things even more, by showing some + capabilities to support First Order Logic - still one of the key + differentiators here is in the design approach to solving this problem; as + an Inference Engine can handle far more complex rules involving many facts + and quantifiers in a stateful manner which may not be possible to express + in SQL and if it was would likely bring an SQL Engine to its knees - where + as an Inference Engine's design approach caters for First Order Logic. + Further to this an Inference Engine is stateful and thus reacts to changes + in data, as part of a Productions Rules System, which enables the logical + relationships between the results of the actions inferred by the data and + rules - this is called Truth Maintenance - .A Query Engine simply returns + results, so it has no support for Truth Maintenance. For example the + "Honest Politician" example, which always ensures that hope and decomcracy + exists.</para> + + <programlisting>when + an honest Politician exists +then + logically assert Hope + +when + Hope exists +then + print "Hurrah!!! Democracy Lives" + +when + Hope does not exist +then + print "Democracy is Doomed" +</programlisting> + + <para></para> + + <para></para> + + <para>To help gain a better understanding we can build a simple linear + rule engine</para> + + <programlisting>// Interface for Production Rules +public interface Rule { + public void evaluate(Object[] objects); +} +</programlisting> + + <programlisting>public class LinearInferenceEngine { + Rules[] rules; + + public LinearInferenceEngine(Rule[] rules) { + this.rules = rules; + } + + public void evaluate(Object[] objects) { + for ( int i = 0; i < rules.length; i++ ) { + rules[i].evaluate( objects ); + } + } +}</programlisting> + + <programlisting>// The production rule - a simple proposition +public class FindMalePeople implements Rule { + public evaluate(Object[] objects) { + Person person = ( Person ) objects[i]; + if ( person.getSex().equals( "male" ) { + consequence( people[i] ); + } + } + + // The consequence + private void consequence(Person person) { + System.out.println(Person.getName() + " is male" ); + } +} +</programlisting> + + <programlisting>// The Facts +Person[] people = new Person[] { new Person("mark", "male"), new Person("bob", "male"), new Person("janet", "female") }; + +// The Production Rules +Rule rules[] = new Rule[] { new FindMalePeople() }; + +/ The Linear Inference Engine +LinearInferenceEngine engine = new LinearInferenceEngine( rules ); + +// Evaluate Facts against the Productions Rules using the Linear Inference Engine +for ( int i = 0; i < people.length; i++ ) { + engine.evaluate( new Object[] { people[i] } ); +}</programlisting> + + <para>However what differentiates a Rule Engine from SQL is its ability to + deal with First Order Logic and actions</para> + + <figure> + <title>Additional Reading</title> + + <itemizedlist> + <listitem> + <para>"Expert Systems - Principles and Programming" (2005) by Joseph + C. Giarratano, Gary D. Riley</para> + </listitem> + + <listitem> + <para>"Introduction to Expert Systems" (1999) by Peter + Jackson</para> + </listitem> + + <listitem> + <para>"Knowledge Representation and Reasing" (2004) by Ronald J. + Brachman, Hector J. Levesque</para> + </listitem> + </itemizedlist> + </figure> + + <para>A Rule Engine like Drools is centred around the concept of + logicproduction rules (IF conditions THEN actions). Emil Leon Post (<ulink + url="http://en.wikipedia.org/wiki/Emil_Leon_Post">http://en.wikipedia.org/wiki/Emil_Leon_Post)</ulink> + was the first to use production systems in logic - as a consequence of + this he was able to prove that any logical system (including mathematics) + could be written with production rules. Quite an amazing proof, this + essentially means that you can represent any system/problem with + production rules (back in the real world there are constraints of + processing power and efficiency of course !).</para> + + <para>You may also have heard of "Expert Systems" in the context of rule + engines. You can think of expert systems as a rule engine + the + codification of knowledge for a specific problem domain (+ a user + interface !). In that sense, Drools is a platform for expert systems. + Historically, rule engines popularity grew out of expert systems success + (for example, EMYCIN was one of the first "shells" for an expert system, + which was created from the MYCIN medical diagnosis expert system).</para> + </section> + + <section> + <title>Rules</title> + + <para>A <indexterm> + <primary>Rule</primary> + </indexterm>Rule is the codification of business knowledge. A Rule has + attributes, a Left Hand Side (LHS) and a Right Hand Side (RHS). Drools + allows the following attributes:</para> + + <itemizedlist> + <listitem> + <para>salience</para> + </listitem> + + <listitem> + <para>agenda-group</para> + </listitem> + + <listitem> + <para>no-loop</para> + </listitem> + + <listitem> + <para>auto-focus</para> + </listitem> + + <listitem> + <para>duration</para> + </listitem> + </itemizedlist> + + <programlisting>rule “<name>” + <attribute> <value> + when + <LHS> + then + <RHS> +end +</programlisting> + + <para>The LHS of the Rule consists of one or more Conditions. As the Rule + Engine is made aware of new data or changes to existing data it matches + the data against the Conditions, when all the Conditions are met and true + the RHS, and its actions, are executed; the RHS, called the <indexterm> + <primary>Consequence</primary> + </indexterm>Consequence. The LHS and the RHS is analogous to:</para> + + <programlisting>if ( <LHS> ) { + <RHS> +}</programlisting> + + <para>Rules are associated with a namespace via the + <literal>package</literal> keyword; other Rule Engines may call this a + <indexterm> + <primary>Rule Set</primary> + </indexterm>Rule Set. A Package declares imports, global variables, + functions and rules.</para> + + <programlisting>package com.sample + +import java.util.List +import com.sample.Cheese + +global List cheeses + +function void exampleFunction(Cheese cheese) { + System.out.println( cheese ); +} + +rule “A Cheesy Rule” + when + cheese : Cheese( type == "stilton" ) + then + exampleFunction( cheese ); + cheeses.add( cheese ); +end</programlisting> + + <para>The process of matching the new or existing modified data against + rules is called <indexterm> + <primary>Pattern Matching</primary> + </indexterm> Pattern Matching, the engine which does this matching is + the <indexterm> + <primary>Inference Engine</primary> + </indexterm>Inference Engine. The Rule's are referred to as the + <indexterm> + <primary>Production Memory</primary> + </indexterm>Production Memory and the data that the Inference Engine + matches against is the <indexterm> + <primary>WorkingMemory</primary> + </indexterm>Working Memory. the Agenda manages the execution of the + matched Rules. There are a number of algorithms used for Pattern Matching + by Inference Engines including:</para> + + <itemizedlist> + <listitem> + <para>Linear</para> + </listitem> + + <listitem> + <para>Rete</para> + </listitem> + + <listitem> + <para>Treat</para> + </listitem> + + <listitem> + <para>Leaps</para> + </listitem> + </itemizedlist> + + <para>Drools has implementations for both Rete and Leaps; Leaps is still + considered experiment. The Drools <indexterm> + <primary>Rete</primary> + </indexterm>Rete implementation is called ReteOO signifying that this + Drools has an enhanced and optimised implementation of the Rete algorithm. + Other Rete based engines also have marketting terms for their prioprietary + enhancements to Rete, like RetePlus and Rete III,</para> + + <figure> + <title>A Basic Rete network</title> + + <mediaobject> + <imageobject> + <imagedata align="center" fileref="Rule_Engine.svg" format="SVG" /> + </imageobject> + + <imageobject> + <imagedata align="center" fileref="Rule_Engine.png" format="PNG" /> + </imageobject> + </mediaobject> + </figure> + + <para>The LHS of a rule is made up of <indexterm> + <primary>Conditional Element</primary> + </indexterm>iConditional Elements and <indexterm> + <primary>Field Constraint</primary> + </indexterm>iField Constraints. The following example shows a <indexterm> + <primary>Literal Field Constraint</primary> + </indexterm>iLiteral Field Constraint used with a Cheese Fact; the + combination of Field Constraints on a Fact is known as a <indexterm> + <primary>Column</primary> + </indexterm>Column.</para> + + <programlisting>rule "Cheddar Cheese" + when + Cheese( type == "cheddar" ) + then + System.out.println( "cheddar" ); +end</programlisting> + + <para>The example above is similar to :</para> + + <programlisting>public void cheddarCheese(Cheese cheese) { + if ( cheese.getType().equals("cheddar") { + System.out.println( "cheddar" ); + } +}</programlisting> + + <para>Rule engines are a complete de-coupling of data from the logic. + Rules cannot be called directly as they are not methods or functions + instead Rules fire in response to changes in Working Memory's data, It may + help to think of this de-coupling as a specialised event sytem. The + Consequence is the Listener to the full matching of the LHS events.</para> + + <para>Rule Engines are much like a database where Rule's LHS define the + queries on the Working Memory. The previous rule can be expressed in + <indexterm> + <primary>SQL</primary> + </indexterm>SQL as:</para> + + <programlisting> select * from Cheese where type == "cheddar"</programlisting> + + <para>A <indexterm> + <primary>DataBase</primary> + </indexterm>Database executes SQL, on request, where as a Rule Engine + will process data against its rules, its Production Memory, as it's + asserted; this process is known as <indexterm> + <primary>Pattern Matching</primary> + </indexterm>Pattern Matching. When added to the Production Memory, + Rule's are decomposed into a graph using the <indexterm> + <primary>Rete</primary> + </indexterm>Rete algorithm. Rete is one of the standard Rule Engine + algorithms developed by <indexterm> + <primary>Charles Forgey</primary> + </indexterm>Charles Forgey in 1979 which is covered in greater detail in + a Rete Algorithm chapter.</para> + + <figure> + <title>A Basic Rete network</title> + + <mediaobject> + <imageobject> + <imagedata align="center" fileref="A_Basic_Rete_Network.svg" + format="SVG" /> + </imageobject> + + <imageobject> + <imagedata align="center" fileref="A_Basic_Rete_Network.png" + format="PNG" /> + </imageobject> + </mediaobject> + </figure> + + <para>Each Fact type in our Working Memory, such as <code>Cheese</code>, + is represented by an <indexterm> + <primary>Object Type</primary> + </indexterm>Object Type class, shown as the root node in our graph. When + Facts are asserted into the Working Memory the Rule Engine finds the + matching Object Type node and propagates the asserted Fact onto the next + node. The Object Type node maintains a memory of all matched Facts. in our + example the next node in the graph is a <indexterm> + <primary>Field Constraint</primary> + </indexterm>Field Constraint, <code>type == "cheddar", </code>its job is + to filter Facts using the given constraint; if the type of + <code>Cheese</code> is not "cheddar" the Fact progresses no further in the + network, if it is "cheddar" it is rememebered in the <indexterm> + <primary>Alpha Node</primary> + </indexterm>Alpha Node's memory and propagated onto the next node in the + network.. Alpha Node is classic Rete terminology for single input/single + output nodes, in that it receives a single Fact of a specified Object Type + and propates a single Fact of specified Object Type.</para> + + <para>At this point we have what is known as a <indexterm> + <primary>Partial Match</primary> + </indexterm>Partial Match, in that we have matched facts against some, + but not all, of the Rule's nodes. <indexterm> + <primary>Left Input Adapter Node</primary> + </indexterm>s will be explained later, suffice to say it always + propagetes onto the next node, in this case a <indexterm> + <primary>Terminal Node</primary> + </indexterm>Terminal Node. The Terminal Node is our end node, now we say + the Rule is Fully Matched and ready to fire.</para> + + <para>Earlier we mentioned that a Rule Engine is much like a Database, we + can prove this by using a <indexterm> + <primary>Query</primary> + </indexterm>Query construct. A Query is Rule with a special Terminal + node; instead of executing a Consequence the Terminal node stores matching + Facts in a list, which is returned as the result. Lets prove this</para> + + <programlisting>query "Find cheeses with a cost of 5" + Cheese( price == 5 ) +end</programlisting> + + <programlisting>// First create the facts +Cheese stilton = new Cheese("stilton", 8); // type, price +Cheese cheddar = new Cheese("cheddar", 5); // type, price +Cheese mozarella = new Cheese("mozarella", 5); // type, price + +// Now assert them into the Working Memory +workingMemory.assertObject( stilton ); +workingMemory.assertObject( cheddar ); +workingMemory.assertObject( mozarella ); + +List results = workingMemory.getQueryResults( "Find cheeses with a cost of 5" );</programlisting> + + <para>When we get the Query Results the List has a size of two and + references "cheddar" and "mozarella", as expected. If we had used a Rule + construct instead of a Query the Terminal Node's Consequence would have + attempted to fire twice, once for "cheddar" and once for + "mozarella".</para> + + <para>When a Rule is Fully Matched it does not fire immediately (in Rete, + but in Leaps it does !). Instead the Rule plus the matched Facts are + <indexterm> + <primary>Activated</primary> + </indexterm>Activated placed onto the <indexterm> + <primary>Agenda</primary> + </indexterm>Agenda; which is responsible for the scheduling and firing + <indexterm> + <primary>Activation</primary> + </indexterm>Activations.</para> + </section> +</section> \ No newline at end of file Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Inference_Engine.png =================================================================== (Binary files differ) Property changes on: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Inference_Engine.png ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Rule_Engine/Inference_Engine.svg =================================================================== --- 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dy="1.2em" class="st5">Engine</tspan></text> </g> + <g id="shape17-15" v:mID="17" v:groupContext="shape" transform="translate(164.017,-0.24)"> + <title>Rectangle.22</title> + <desc>Conclusions</desc> + <v:userDefs> + <v:ud v:nameU="visVersion" v:val="VT0(11):26"/> + </v:userDefs> + <v:textBlock v:margins="rect(4,4,4,4)" v:tabSpace="42.5197"/> + <v:textRect cx="70.8661" cy="262.357" width="141.74" height="51.0236"/> + <rect x="0" y="236.845" width="141.732" height="51.0236" class="st4"/> + <text x="16.4" y="268.36" class="st3" v:langID="2057"><v:paragraph v:horizAlign="1"/><v:tabList/>Conclusions</text> </g> + <g id="shape18-18" v:mID="18" v:groupContext="shape" v:layerMember="1" transform="translate(100.542,-237.238)"> + <title>Dynamic connector.18</title> + <v:userDefs> + <v:ud v:nameU="visVersion" v:val="VT0(11):26"/> + </v:userDefs> + <path d="M0 287.87 L10.63 287.87 A10.6299 10.6299 0 0 1 21.26 298.5 L21.26 338.61 A19.6902 19.6902 -180 0 0 40.95 358.3 + L52.36 358.3" class="st6"/> 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