1. Balaji Venugopal Vimal Kumar

2. Definition
A rule engine is a piece of software, which having some
knowledge is able to perform conclusions.
Rules Engines are the pluggable software components
that separate the business rules from the application code
• A system that attempts to act as a domain
expert

3. Some frequently asked questions
Why use a Rule Engine?
When should I use a rule engine?
What advantage does a rule engine have
over hand coded "if...then" approaches?
We will attempt to address these
questions…….

4. Why use a Rule Engine?
If “Procedural Code” is available then why RULE ENGINE ?
The Problem
• Have you developed/seen code with so many nested if statements that
actually looked like a nest?

5. Why use a Rule Engine?
The Problem (Contd…)
• Have you spent sleepless nights debugging code when you modified one
of those 'if' statements and found that it distorted the next if statement?
• Ever wondered how others implement these?

6. Why use a Rule Engine?
The Solution
• “Rule engines are a great way to collect complex decision-making logic
and work with data sets too large for humans to effectively use.”

7. When should I use a Rule Engine?
"when there is no satisfactory traditional programming approach to solve the problem."
Complicated logic (not 1+1 = 2)‫‏‬

8. The Robot Example
Teaching a robot to prepare breakfast cereal (in an optimal environment)
START
putOnTable(bowl)
putOnTable(spoon)
putOnTable(napkin)
open(cereal-container)
pour(cereal)
open(milk-container)
pour(milk)
inviteOwner("You may begin eating")
END

9. The Robot Example
Teaching a robot to drive is much more difficult because the task vary
according
to environmental conditions
a. Robot must not do the tasks for backing out of garage if the garage door is
closed or if there is a little girl on a tricycle right behind the car
b. Backing out will be different on warm days and cold days. Also, in a
parking spaces, backing up procedures will be different in different
Settings
c. So many different circumstances lead to endless logic branching
d. Solution: independent rules that are integrated in an inference engine:
IF the engine has stalled
THEN start car
IF you hear sirens
THEN pull over to the curb
etc.

10. When should you use a Rule Engine?
Changes often (whatever that means)‫‏‬
Traditional approaches are unmaintainable
The problem is beyond any obvious algorithmic solution
Domain experts (or business analysts) are readily available, but are
nontechnical

11. Advantages of a Rule Engine
Declarative Programming
Logic and Data Separation
Speed and Scalability
Centralization of Knowledge
Tool Integration
Explanation Facility

12. High Level view of a Rule Engine

14. Working Memory. The data that rules work on. Also called the fact base.
Production Memory. Knowledge and inferences are stored in rules, which
are called production rules.
Pattern Matcher. Determines which rules to apply to the data in working
memory.
• There are a number of algorithms used for Pattern Matching by Inference
Engines including:
• Linear
• Rete
• Treat
• Leaps
Agenda. Determines the order of execution of rules.
Execution Engine. The part of the rules engine that is responsible for
applying rules to data and then performing the action part of the rules
that have fired.

15. Architecture of Typical Rule Based
System
observed data
working memory
select modify
rule
Inference
memory fire output
engine

16. Reasoning with production rules
 Architecture of a typical production
system:
New information
working memory
select modify
rule
interpreter
memory fire output

17. Reasoning with production rules
 Architecture of a typical production
system:
New information
select working memory
modify
rule
interpreter
memory fire output

18. Reasoning with production rules
 Architecture of a typical production
system:
New information
working memory
select
modify
Inference
rule engine
memory output
fire executes
actions

19. Reasoning with production rules
 Architecture of a typical production
system:
New information
working memory
select modify
Inference
rule engine
memory
fire executes output
actions

20. Reasoning with production rules
 Architecture of a typical production
system:
New information
select working memory
modify
rule
interpreter
memory fire output

21. Reasoning with production rules
 Architecture of a typical production
system:
New information
working memory
select
modify
Inference
rule engine
memory executes output
fire
actions

22. Reasoning with production rules
 Architecture of a typical production
system:
New information
working memory
select modify
Inference
rule engine
memory executes
fire output
actions

23. Methods of execution for a rule system
Forward Chaining Backward Chaining
Goal Goal
Start
Start

24. Forward Chaining
Goal
conclude the color of my pet Fritz
Working Memory Rule Base
he croaks 1. If X croaks and eats flies - Then X is a frog
eats flies 2. If X chirps and sings - Then X is a canary
3. If X is a frog - Then X is green
4. If X is a canary - Then X is yellow

25. Working Memory Rule Base
he croaks 1. If X croaks and eats flies - Then X is a frog
eats flies 2. If X chirps and sings - Then X is a canary
3. If X is a frog - Then X is green
4. If X is a canary - Then X is yellow
the color of my pet Fritz Working Memory
GREEN he croaks
eats flies
frog

26. Backward Chaining
Goal
conclude the color of my pet Fritz
Working Memory Rule Base
he croaks 1. If X croaks and eats flies - Then X is a frog
eats flies 2. If X is a frog - Then X is green
3. If X is a canary - Then X is yellow

27. Goal List Rule Base
1. conclude the color of my pet 1. If X croaks and eats flies - Then X is a frog
Fritz 2. If X chirps and sings - Then X is a canary
3. If X is a frog - Then X is green
4. If X is a canary - Then X is yellow
Goal List
Working Memory
1. conclude the color of my pet Fritz
he croaks 2. X is a frog
eats flies 3. X is a canary
So my pet Fritz
Is frog Is Green He croaks and eats flies

28. Solutions Which Are Attracting The Use Of
Business Rules For More Efficiency
• Marketing/Campaign • Business Process/Workflow
• Management Account • Data Validation/Formatting
• Management/Personalization • Self-Service Web Inquiries
Behavior Scoring
• Regulatory Compliance
• Product / Service
Recommendation • Order Configuration
• Underwriting • Call Center/CRM
(Lending/Insurance) • Fraud Detection
• Diagnostics/Problem Resolution • Authorization
• Sales Commission Calculation • Benefits Analysis
• Pension Portfolio Marketing
• Manufacturing Process
• Risk Analysis
• Claims Processing
• Compliance Enforcement
• Database Migration Tool.

29. Rule Engines
Drools
BizTalk
Blaze Advisor
Jess
JRules
OpenRules
PegaRules
RulesPower

30. Heart of the System