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.

1. www.synerzip.com
JBOSS DROOLS
RULE ENGINE
Anil Allewar
1

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. 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. 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. 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. 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. 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. Sample Drools Rule
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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. 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. Drools Operators
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 < <= > >=
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. Drools Conditional Elements
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 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. Drools Conditional Elements
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 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. The Problem!!
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 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. Demo
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 Source code available at
https://github.com/anilallewar/drools-Example

15. Using Timers
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 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. 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. Knowledge Agents
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 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. Decision Tables
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 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. Drools Flow
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 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. Other Drools Offerings
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 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. 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. Questions?