1 programming a knowledge based application. 2 overview
TRANSCRIPT
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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)