JBoss Drools - Pure Java Rule Engine


Published on

Drools is a Rule Engine that uses the rule-based approach to implement an Expert System

The inference engine matches the rules against the facts (objects) in memory and can match the next set of rules based on the changed facts.

Please use the presentation and the source code referred in the presentation to get started on what a rule engine is and how to use JBoss Drools for inference based rules using the Java programming language.

Published in: Software
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Stateless Knowledge Session examples
    1. Validation - Is this person eligible for a mortgage?
    2. Calculation - Compute a mortgage premium.
    3. Routing and Filtering - Filter incoming messages, such as emails, into folders. Send incoming messages to a destination.

    Stateful Knowledge Session examples
    1. Monitoring - Stock market monitoring and analysis for semi-automatic buying.
    2. Diagnostics - Fault finding, medical diagnostics
    3. Logistics - Parcel tracking and delivery provisioning
    4. Compliance - Validation of legality for market trades.
  • Globals are not inserted into the Working Memory, and therefore a global should never be used to establish conditions in rules except when it has a constant immutable value.

    If multiple packages declare globals with the same identifier they must be of the same type and all of them will reference the same global value.

    All operators have normal Java semantics except for == and !=. The == operator has null-safe equals() semantics i.e. it is equivalent to equals() method.
  • Soundslike - checks whether a word has almost the same sound (using English pronunciation) as the given value.
    str - This operator str is used to check whether a field that is a String starts with or ends with a certain value. It can also be used to check the length of the String.
  • forall - forall evaluates to true when all facts that match the first pattern match all the remaining patterns. In the above rule, we "select" all Bus objects whose type is "english". Then, for each fact that matches this pattern we evaluate the following patterns and if they match, the forall CE will evaluate to true.
    eval - The conditional element eval is essentially a catch-all which allows any semantic code (that returns a primitive boolean) to be executed.
  • from - from will iterate over all objects in the collection and try to match each of them individually. The rule gets fired for each item that is matched.
    collect - Allows us to reason over a collection of objects. In the above example, the rule will look for all pending alarms in the working memory for each given system and group them in ArrayLists. If 3 or more alarms are found for a given system, the rule will fire. The result pattern of collect can be any concrete class that implements the java.util.Collection interface and provides a default no-arg public constructor.
    accumulate - Allows a rule to iterate over a collection of objects, executing custom actions for each of the elements, and at the end it returns a result object. Drools ships with the following built-in accumulate functions:
    You can build your own accumulate functions by implementing org.drools.runtime.rule.AccumulateFunction interface and add a line to the configuration file or set a system property to let the engine know about the new function.

  • Please refer to the “drools-Example” project for the demo.
  • JBoss Drools - Pure Java Rule Engine

    1. 1. www.synerzip.com JBOSS DROOLS RULE ENGINE Anil Allewar 1
    2. 2. Agenda 2 1. Introduction to Knowledge based Rule Engine 2. Basics of Drools rules 3. Drools Operators 4. Drools Conditional Elements 5. The problem - Fire Alarm Management System 6. Drools Demo 7. How to control execution of rules - timers 8. Drools integration into Java - using knowledge agent, changeset 9. Introduction to decision tables 10. Introduction to Drools Flow for workflow 11. A very brief introduction to Drools Guvnor, Fusion and Planner
    3. 3. Rule Engine? 3  Drools is a Rule Engine that uses the rule- based approach to implement an Expert System  A Production Rule is a two-part structure using First Order Logic for reasoning over knowledge representation.  The inference engine matches the rules against the facts (objects) in memory when <conditions> then <actions>;
    4. 4. Rule Engine? 4 •The rules are loaded into production memory and are available at all times •Facts are asserted into the Working Memory where they may then be modified or retracted. •The Agenda manages the execution order of the conflicting rules using a conflict resolution strategy. •The rules might be in conflict when more than 1 rule matches the same set of facts in working memory
    5. 5. Backward Vs Forward Chaining 5  A forward chaining engine looks at the facts and derives a conclusion Consider a scenario of medical diagnosis => If the patient’s symptoms are put as facts into working memory, then we can diagnose him with an ailment. When nasal congestion && fever && body ache Then Influensa Working memory 1. body ache 2. Fever 3. Nasal congestion INFLUENZA
    6. 6. Backward Vs Forward Chaining 6  A backward chaining engine has the “goal” specified and the engine tries to satisfy it. Consider the same scenario of medical diagnosis => if there is an epidemic of a certain disease, this AI could presume a given individual had the disease and attempt to determine if its diagnosis is correct based on available information. Goal Influensa Sub-goal nasal congestion fever body ache Working memory 1. body ache 2. fever NO INFLUENZA
    7. 7. Drools Basics 7  Knowledge Sessions Stateless  Doesn’t maintain reference to objects after first call and can be thought of as plain functions  Typical use cases include validation, routing etc Stateful  Longer lived, maintain reference to objects and allow iterative changes over time  Typical use cases include diagnostics, monitoring etc  In contrast to a Stateless Session, the dispose() method must be called afterwards to ensure there are no memory leaks.  Facts Facts are objects that are inserted/modified/retracted from working memory AND is the data on which the rules act. "logicalInsert" => Here the fact is logically inserted, this fact is dependant on the truth of the "when" clause. It means that when the rule becomes false the fact is automatically retracted.  A rule while firing can change the state of the working memory thereby causing other rules to fire.
    8. 8. Sample Drools Rule 8 When part package com.anil.drools.service import com.anil.drools.model.Fire; import com.anil.drools.model.Alarm; global Logger LOGGER; rule "Raise the alarm when there is at least 1 Fire" salience 100 lock-on-active true when exists Fire() then insert (new Alarm()); LOGGER.debug( "Raised the alarm because at least 1 Fire() object exists in the session" ); end Rule Name Attributes Then part Package Name (Must be 1st element if declared) Import java types (referenced by rules) Global variables
    9. 9. Rule Attributes 9  Rule attributes provide a declarative way to influence the behavior of the rule. no-loop  When a rule's consequence modifies a fact it may cause the rule to activate again, causing an infinite loop. lock-on-active  This is a stronger version of no-loop, because the change could now be caused not only by the rule itself but by other rules too. Salience  Salience is a form of priority where rules(all of whom match) with higher salience values are given higher priority when ordered in the Activation queue. agenda-group  Only rules in the agenda group that has acquired the focus are allowed to fire. Refer to Drools documentation for additional attributes
    10. 10. Drools Operators 10  < <= > >= Person( firstName < $otherFirstName )  [not] matches (against Java regex) Cheese( type matches "(Buffalo)?S*Mozarella" )  [not] contains (check field within array/collection) CheeseCounter( cheeses contains "stilton" )  soundslike // match cheese "fubar" or "foobar" Cheese( name soundslike 'foobar' )  str Message( routingValue str[startsWith] "R1" )  [not] in Cheese( type in ( "stilton", "cheddar", $cheese ) )
    11. 11. Drools Conditional Elements 11  and / or Cheese( cheeseType : type ) and Person( favouriteCheese == cheeseType ) Cheese( cheeseType : type ) or Person( favouriteCheese == cheeseType )  not not Bus(color == "red")  exists exists Bus(color == "red")  forall forall( $bus : Bus( type == 'english') Bus( this == $bus, color = 'red' ) )  eval eval( p1.getList().containsKey( p2.getItem() ) )
    12. 12. Drools Conditional Elements 12  from $order : Order() $item : OrderItem( value > 100 ) from $order.items  collect $system : System() $alarms : ArrayList( size >= 3 ) from collect( Alarm( system == $system, status == 'pending' ) )  accumulate $order : Order() $total : Number( doubleValue > 100 ) from accumulate( OrderItem( order == $order, $value : value ), sum( $value ) ) weeklyVariance : Number( ) from accumulate (Number( valueReturned : doubleValue) from ruleVO.varianceList, sum(valueReturned))
    13. 13. The Problem!! 13  Fire Alarm Mgmt System Everyone is happy if there is no fire If there is fire in any room, set an alarm If there is fire in a room, turn ON sprinkler for that room Once the fire extinguishes, turn OFF sprinkler for that room If there is NO fire and sprinklers are off; tell everyone to get back to being happy 
    14. 14. Demo 14  Source code available at https://github.com/anilallewar/drools-Example
    15. 15. Using Timers 15  Rules support both interval and cron based timers modeled on Quartz. rule "Send SMS every 15 minutes" timer (cron:* 0/15 * * * ?) when $a : Alarm( on == true ) then channels[ "sms" ].insert( new Sms( $a.mobileNumber, "The alarm is still on" ); end
    16. 16. More On Deploying 16  Changesets Configuration to build the knowledgebase Use an XML that contains a list of resources and can contain reference to another changeset (recursive changesets)<change-set xmlns='http://drools.org/drools-5.0/change-set' xmlns:xs='http://www.w3.org/2001/XMLSchema-instance' xs:schemaLocation='http://drools.org/drools-5.0/change-set http://anonsvn.jboss.org/repos/labs/labs/jbossrules/trunk/drools- api/src/main/resources/change-set-1.0.0.xsd' > <add> <resource source='http://fqng-app02-dev-jboss:8080/drools- guvnor/org.drools.guvnor.Guvnor/package/fqAlarmWorkflow/LATEST' type='PKG' basicAuthentication=‘enabled’ username=‘admin’ password=‘’/> </add> </change-set>
    17. 17. Knowledge Agents 17  The Knowlege Agent provides automatic loading, caching and re-loading of resources and is configured from a properties files OR KnowledgeAgentConfiguration.  A KnowledgeAgent object will continuously scan all the added resources, using a default polling interval of 60 seconds(can be changd) and, when some last modification date is updated, it will applied the changes into the cached Knowledge Base using the new resources.  For polling to occur, the polling and notifier services must be started. ResourceFactory.getResourceChangeNotifierService().start(); ResourceFactory.getResourceChangeScannerService().start();
    18. 18. Decision Tables 18  Managing rules in a spreadsheet format  In a decision table each row is a rule, and each column in that row is either a condition or action for that rule. RuleSet com.anil.drools.decisiontable Import com.anil.drools.model.decisiontable.Driver, com.anil.drools.model.decisiontable.Policy Variables Notes Decision tables for policy prices RuleTable policy prices POLICY NAME CONDITION CONDITION CONDITION CONDITION ACTION ACTION $driver : Driver $policy : Policy age >=$1 && age<=$2 locationRiskProfile numberOfPriorClaims policyType $policy.setPolicyBasePrice($param); System.out.println("$param"); Name Driver Age Bracket Location Risk Profile Number of Prior Claims Insurance Policy Type Base $ price Reason Young Safe driver 18,24 LOW 1 COMPREHENSIVE 490.00 1 prior claims 18,24 MED FIRE_THEFT 56.00 Fire theft medium 18,24 MED COMPREHENSIVE 700.00 Comprehensive medium 18,24 LOW 2 FIRE_THEFT 250.00 2 prior claims 18,24 LOW 0 COMPREHENSIVE 400.00 Safe driver discount Mature Drivers 25,60 LOW 1 COMPREHENSIVE 420.00 mature - 1 prior claims 25,60 MED FIRE_THEFT 37.00 mature - Fire theft medium 25,60 MED COMPREHENSIVE 645.00 mature - Comprehensive medium 25,60 LOW 2 FIRE_THEFT 234.00 mature - 2 prior claims 25,60 LOW 0 COMPREHENSIVE 356.00 mature - Safe driver discount
    19. 19. Drools Flow 19  Drools flow is used in conjuction with Drools Expert to specify the flow of business rules.  The nodes are specified by the ruleflow-group rule attribute.  As of Drools 5, Drools flow is going to be combined with jBPM and is renamed as jBPM 5.0.
    20. 20. Other Drools Offerings 20  Guvnor Guvnor is the Drools business rule management system that allows people to manage rules in a multi user environment, it is a single point of truth for your business rules, allowing change in a controlled fashion, with user friendly interfaces. The Guvnor combined with the core drools engine and other tools forms the business rules manager. The data can be stored with multiple persistence schemas (file, database etc) using the JackRabbit JCR (Java content repository) as the underlying implementation. Guvnor offers versioning of rules, authentication and authorization to limit users to what they can do.
    21. 21. Other Drools Offerings 21  Planner Drools Planner optimizes planning problems. It solves use cases, such as:  Employee shift rostering: rostering nurses, repairmen, …  Agenda scheduling: scheduling meetings, appointments, maintenance jobs, advertisements, …  Educational timetabling: scheduling lessons, courses, exams, conference presentations, ...  Fusion Drools Fusion supports complex event processing It deals with the tasks of handling multiple events nearly at real- time with the goal of identifying the meaningful events within the event cloud. Events, from a Drools perspective are just a special type of fact. In this way, we can say that all events are facts, but not all facts are events.
    22. 22. Questions?