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Programming a Knowledge Based Application

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Overview

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Rule-based Intelligent UI

Inference EngineKnowledge Base (Rules)

WorkingMemory (Facts)

User Interface

Agenda

“Intelligence”(Knowledge-based system)

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Rule-based Intelligent UI

Inference EngineKnowledge Base (Rules)

WorkingMemory (Facts)

User Interface

Agenda

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Components of a Rule-Based System (1)

FACT BASE or fact list represents the initial state of the problem. This is the data from which inferences are derived.

RULE BASE or knowledge base (KB) contains a set of rules which can transform the problem state into a solution. It is the set of all rules.

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Components of a Rule-Based Language (2)

INFERENCE ENGINE controls overall execution. It matches the facts against the rules to see what rules are applicable. It works in a recognize-act cycle: match the facts against the rules choose which rules instantiation to fire execute the actions associated with the

rule

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CLIPS C Language Integrated Production System Public domain software Supports:

Forward Chaining Rules based on Rete algorithm Procedural Programming Object-oriented programming (COOL)

Can be integrated with other C/C++ programs/applications

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JESS Java Expert System Shell Inspired by CLIPS => forward chaining rule

system + Rete algorithm Free demo version available (trial period of

30 days) Can be integrated with other Java code

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Bakcground

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Production Rules

Production rules were developed for use in automata theory, formal grammars, programming language design & used for psychological modeling before they were used for expert systems.

Also called condition-action, or situation-action rules.

Encode associations between patterns of data given to the system & the actions the system should perform as a consequence.

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Canonical Systems

Production rules are grammar rules for manipulating strings of symbols.

Also called rewrite rules (they rewrite one string into another).

First developed by Post (1943), who studied the properties of rule systems based on productions & called his systems canonical systems.

He proved any system of mathematics or logic could be written as a type of production rule system. Minsky showed that any formal system can be realized as a canonical system.

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Example of a Canonical System

Let A be the alphabet {a, b, c} With axioms a, b, c, aa, bb, cc Then these production rules will give all the

possible palindromes (and only palindromes) based on the alphabet, starting from the above axioms. (P1) $ -> a$a (P2) $ -> b$b (P3) $ -> c$c

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Example continued

To generate bacab P1 is applied to the axiom c to get aca Then we apply P2 to get bacab Using a different order gives a

different result. If P2 is applied to c we get bcb If P1 is applied after we get abcba

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Production Systems for Problem Solving

In KB systems production rules are used to manipulate symbol structures rather than strings of symbols.

The alphabet of canonical systems is replaced by a vocabulary of symbols and a grammar for forming symbol

structures.

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Rule-Based Production Systems

A production system consists of a rule set / knowledge base / production memory a rule interpreter / inference engine

that decides when to apply which rules a working memory

that holds the data, goal statements, & intermediate results that make up the current state of the problem.

Rules have the general formIF <pattern> THEN <action>

P1, …, Pm Q1, …, Qn

Patterns are usually represented by OAV vectors.

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An OAV Table

Object Attribute Value

Beluga Whale Dorsal Fin No

Beluga Whale Tail Fin No

Blue Whale Tail Fin Yes

Blue Whale Dorsal Fin Yes

Blue Whale Size Very Large

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RULES General form: a b

IF … THEN …IF < antecedent, condition, LHS>THEN <consequent, action, RHS>

Antecedent match against symbol structure

Consequent contains special operator(s) to manipulate those symbol structures

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Syntax of Rules

The vocabulary consists of a set N of names of objects in the domain a set P of property names that give

attributes to objects a set V of values that the attributes can

have. Grammar is usually represented by OAV

triplesOAV triple is (object, attribute, value) triplesExample: (whale, size, large)

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Forward & Backward Chaining

Production rules can be driven forward or backward.

We can chain forward from conditions that we know to be true towards problem states those conditions allow us to establish the goal; or

We can chain backward from a goal state towards the conditions necessary for establishing it.

Forward chaining is associated with ‘bottom-up’ reasoning from facts to goals.

Backward chaining is associated with ‘top-down’ reasoning from facts to goals.

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Forward Backward Chaining Chaining

facts

goal

Fact:Saw dorsal

Fin - NO

Fact:Saw tail fin

-NO

Inferred:Beluga

and

Concrete facts

Saw dorsal Fin - NO

Saw tail fin-NO

Goal:Beluga

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Palindrome Example

If we have the following grammar rules(P1) $ -> a$a(P2) $ -> b$b(P3) $ -> c$c

They can be used to generate palindromes forward chainingapply P1, P1, P3, P2, to c > aca aacaa caacaac -

Or they can be used to recognize palindromes backward chainingbacab matches the RHS of P2 but acbcb will not be

accepted by any RHS

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Forward Chaining

Fig. based on http://ai-depot.com/Tutorial/RuleBased-Methods.html

Determine possible

rules to fire

Select rule to

fire

Conflictresolutionstrategy

Exit

No rule found

Conflict set

Fire rule

Rule found

Exit if specified by the rule

Rulebase

Workingmemory

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Chaining & CLIPS/JESS

CLIPS and JESS uses forward chaining. The LHS of rules are matched against

working memory. Then the action described in the RHS

of the rule, that fires after conflict resolution, is performed.

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Palindrom Example

To generate bacab P1 is applied to the axiom c to get aca Then we apply P2 to get bacab Using a different order gives a

different result. If P2 is applied to c we get bcb If P1 is applied after we get abcba

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Markov algorithm

A Markov algorithm (1954) is a string rewriting system that uses grammar-like rules to operate on strings of symbols. Markov algorithms have been shown to have sufficient power to be a general model of computation.

Important difference from canonical system: now the set of rules is ordered

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Palindrom example revisited

To generate bacab P1 is applied to the axiom c to get aca Then we apply P2 to get bacab Using a different order gives a

different result. If P2 is applied to c we get bcb If P1 is applied after we get abcba

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Making it more efficient The Rete algorithm is an efficient

pattern matching algorithm for implementing rule-based expert systems.

The Rete algorithm was designed by Dr. Charles L. Forgy of Carnegie Mellon University in 1979.

Rete has become the basis for many popular expert systems, including OPS5, CLIPS, and JESS.

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RETE algorithm

Creates a decision tree where each node corresponds to a pattern occurring at the left-hand side of a rule

Each node has a memory of facts that satisfy the pattern

Complete LHS as defined by a path from root to a leaf.

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Rete example

(http://aaaprod.gsfc.nasa.gov/teas/Jess/JessUMBC/sld010.htm)

x? y? x? y? z?Pattern Network

Rules: IF x & y THEN p IF x & y & z THEN q

p

Join Network

8 nodesq

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Rete example

(http://aaaprod.gsfc.nasa.gov/teas/Jess/JessUMBC/sld010.htm)

x? y? z?Pattern Network

Rules: IF x & y THEN p IF x & y & z THEN q

p

Join Network

6 nodesq

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Rete example

(http://aaaprod.gsfc.nasa.gov/teas/Jess/JessUMBC/sld010.htm)

x? y? z?Pattern Network

Rules: IF x & y THEN p IF x & y & z THEN q

p

Join Network

5 nodesq

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Matching Patterns

At each cycle the interpreter looks to see which rules have conditions that can be satisfied.

If a condition has no variables it will only be satisfied by an identical expression in working memory.

If the condition contains variables then it will be satisfied if there is an expression in working memory with an attribute-value pair that matches it in a way that is consistent with the way other conditions in the same rule have already been matched.

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Example of matching

(whale (species Beluga) (tail_fin NO)(dorsal_fin NO))

Matches the pattern (with variables)

(whale (species ?name) (tail_fin ?flukes) (dorsal_fin ?fin)

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The Working Memory

Holds data in the form of OAV vectors. These data are then used by the interpreter

to activate the rules. The presence or absence of data elements

in the working memory will trigger rules by satisfying patterns on the LHS of rules.

Actions such as assert or modify the working memory.

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Conflict Resolution

Production systems have a decision-making step between pattern matching & rule firing.

All rules that have their conditions satisfied are put on the agenda in CLIPS.

The set of rules on the agenda is sometimes called the conflict set.

Conflict resolution selects which rule to fire from the agenda.

Packages like CLIPS provide more than one option for conflict resolution

Sensibility (quick response to changes in WM) and Stability (continuous reasoning).

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Conflict Resolution in CLIPS

First, CLIPS uses salience to sort the rules. Then it uses the other strategies to sort rules with equal salience.

CLIPS uses refraction, recency & specificity in the form of following 7 strategies: The depth strategy The breadth strategy The simplicity strategy The complexity strategy The LEX strategy The MEA strategy It is possible also to set strategy to random

Syntax: (set-strategy <strategy>)

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Salience

Normally the agenda acts like a stack. The most recent activation placed on the

agenda is the first rule to fire. Salience allows more important rules to stay

at the top of the agenda regardless of when they were added.

If you do not explicitly say, CLIPS will assume the rule has a salience of 0. a positive salience gives more weight to a rule a negative salience gives less weight to a rule

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Refractoriness

A rule should not be allowed to fire more than once for the same data.

Prevents loops Used in CLIPS and JESS (need to (refresh) to bypass it)

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How to

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Example

Simplified description of some varieties of cultivated apples:

Variety Size Color

Cortland large red

Golden delicious large yellow

Red Delicious large green

Granny Smith medium red

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Rules The simple way – write standard

if..then rules:IF (color == red && size == large)

THEN variety = Cortland

We will need: 4 rules (+ rule(s) for asking questions) => minimum 5 rules

BUT: can do it in 2 rules in CLIPS/JESS

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Define Template

(deftemplate apple(multislot variety (type SYMBOL) )(slot size (type SYMBOL))(slot color (type SYMBOL) (default red)))

Other useful slot type: NUMBERJESS note: in JESS multislots don’t have type

CLIPS allow both SYMBOL and STRING types, JESS – only STRING

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Assert Facts

(deffacts apple_varieties

(apple (variety Cortland) (size large) (color red))

(apple (variety Golden delicious) (size large) (color yellow))

(apple (variety Red Delicious) (size medium) (color red))

(apple (variety Granny Smith) (size large) (color green))

)

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Create Rules – Rule 1(defrule ask-size

(declare (salience 100)) ;NOTE: JESS don’t use salience(initial-fact)

=>(printout t “Please enter the apple characteristics :“  crlf)(printout t “- color (red, yellow, green) : “)(bind ?ans1 (read))(printout t crlf “-size (large or medium) : “)(bind ?ans2 (read))(assert (apple (variety users) (color ?ans1) (size ?ans2)))

)

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Create Rules – Rule 2

(defrule variety(declare (salience 10)) ;JESS NOTE – take this out(apple (variety users) (size ?s) (color ?c))(apple (variety ?v&:(neq ?v users))(size ?s) (color ?c))

=>(printout t “You’ve got a “ ?v crlf)(halt))

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Run CLIPS Type the code in a file, save it (e.g. apples.clp) start CLIPS (type clips or xclips in

UNIX/LINUX) do: File -> Load (in XCLIPS) or type

(load “apples.clp”) when the file is loaded CLIPS will display:

defining deftemplate appledefining deffacts apple_varietiesdefining defrule ask-size +jdefining defrule variety +j+jTRUE

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Run CLIPS Type (reset) to put your initial facts in the fact

base CLIPS>(run)

Please enter the apple characteristics:

- color red, yellow, green: red

- size (large or medium) : large

You’ve got a Cortland

CLIPS> (exit)

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Run JESS UNIX command line:

java –classpath jess.jar jess.Main Or start an applet console.html Jess> (batch apples.clp)

TRUEJess> (reset)TRUEJess> (run)Please enter the apple characteristics:

- color red, yellow, green: red- size (large or medium) : largeYou’ve got a Cortland

2Jess> (exit)

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CLIPS resources Official CLIPS website (maintained by

Gary Riley):http://www.ghg.net/clips/CLIPS.html

CLIPS Documentation:http://www.ghg.net/clips/download/documentation

Examples:

http://www.ghg.net/clips/download/executables/examples/

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Integrating CLIPS into C/C++

Go to the source code Replace CLIPS main with user-defined

main (follow the instructions within the main)

#include “clips.h” in classes that will use it

Compile all with ANSI C++ compiler

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Resources for CLIPS C++ integration CLIPS advanced programming guide Anonymous ftp from hubble.jsc.nasa.gov

directory pub/clips/Documents DLL for CLIPS 5.1 for Windows at

ftp.cs.cmu.edu directory pub/clips/incoming Examples can be found also at

http://ourworld.compuserve.com/homepages/marktoml/cppstuff.htm and http://www.monmouth.com/%7Ekm2580/dlhowto.htm

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JESS resources http://herzberg.ca.sandia.gov/jess/ Includes instructions and examples

for embedding JESS into a Java program (http://herzberg.ca.sandia.gov/jess/docs/61/embedding.html ) and or creating Java GUI from JESS (see http://herzberg.ca.sandia.gov/jess/docs/61/jessgui.html)