Introduction to Expert System Chapter 11. Rule-Based AI

2013/5/2

1

What is an expert system?

“An expert system is a computer system that emulates, or acts in all respects, with the decision-making capabilities of a human expert.”

Professor Edward Feigenbaum

Stanford University

2

Expert System Main Components

• Knowledge base – obtainable from books, magazines, knowledgeable persons, etc.– An expert’s knowledge is specific to one problem

domain – medicine, finance, science, engineering, etc.

• Inference engine – draws conclusions from the knowledge base

3

Basic Functions of Expert Systems

4

Representing the Knowledge

The knowledge of an expert system can be represented in a number of ways,

including IF-THEN rules:

IF you are hungry THEN eat

5

The Goal of Expert Systems

• We need to be able to separate the actual meanings of words with the reasoning process itself.

• We need to make inferences w/o relying on semantics.

• We need to reach valid conclusions based on facts only.

6

Figure 2.2 The Pyramid of Knowledge

7

Productions

A number of knowledge-representation techniques have been devised:

• Rules: IF-THEN rules• Semantic nets• Frames• Scripts• Logic• Conceptual graphs

8

9

Knowledge Engineering

The process of building an expert system:

1. The knowledge engineer establishes a dialog with the human expert to elicit knowledge.

2. The knowledge engineer codes the knowledge explicitly in the knowledge base.

3. The expert evaluates the expert system and gives a critique to the knowledge engineer.

10

Development of an Expert System

11

Structure of aRule-Based Expert System

12

Elements of an Expert System

• User interface – mechanism by which user and system communicate.

• Explanation facility – explains reasoning of expert system to user.

• Working memory – global database of facts used by rules.

• Inference engine – makes inferences deciding which rules are satisfied and prioritizing.

13

Elements Continued

• Agenda – a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory.

• Knowledge acquisition facility – automatic way for the user to enter knowledge in the system bypassing the explicit coding by knowledge engineer.

14

Direct Reasoning Modus Ponens

15

Some Rules of Inference

16

Rules of Inference

17

Production Rules

• Knowledge base is also called production memory.

• Production rules can be expressed in IF-THEN pseudocode format.

• In rule-based systems, the inference engine determines which rule antecedents are satisfied by the facts.

18

Chaining

• Chain – a group of multiple inferences that connect a problem with its solution

• A chain that is searched / traversed from a problem to its solution is called a forward chain.

• A chain traversed from a hypothesis back to the facts that support the hypothesis is a backward chain.

• Problem with backward chaining is find a chain linking the evidence to the hypothesis.

19

General Methods of Inferencing

• Forward chaining – reasoning from facts to the conclusions resulting from those facts – best for prognosis, monitoring, and control.– data-driven

• Backward chaining – reasoning in reverse from a hypothesis, a potential conclusion to be proved to the facts that support the hypothesis – best for diagnosis problems.– goal driven

20

Production Systems

• Rule-based expert systems – most popular type today.

• Knowledge is represented as multiple rules that specify what should/not be concluded from different situations.

• Forward chaining – start w/facts and use rules do draw conclusions/take actions.

• Backward chaining – start w/hypothesis and look for rules that allow hypothesis to be proven true.

21

Figure 3.21 Causal Forward Chaining

22

What is CLIPS?

• CLIPS is a multiparadigm programming language that provides support for:– Rule-based

– Object-oriented

– Procedural programming

• Syntactically, CLIPS resembles:– Eclipse

– CLIPS/R2

– JESS

23

Other CLIPS Characteristics

• CLIPS supports only forward-chaining rules.

• The OOP capabilities of CLIPS are referred to as CLIPS Object-Oriented Language (COOL).

• The procedural language capabilities of CLIPS are similar to languages such as:– C

– Ada

– Pascal

– Lisp

24

CLIPS Characteristics

• CLIPS is an acronym for C Language Integrated Production System.

• CLIPS was designed using the C language at the NASA/Johnson Space Center.

• CLIPS is portable – PC CRAY.

25

• (deftemplate goal (slot move) (slot on-top-of))

• (deffacts initial-state• (stack A B C)• (stack D E F)• (goal (move C) (on-top-of E))• (stack))

• (defrule move-directly• ?goal <- (goal (move ?block1) (on-top-of ?block2))

• ?stack-1 <- (stack ?block1 $?rest1)• ?stack-2 <- (stack ?block2 $?rest2)• =>• (retract ?goal ?stack-1 ?stack-2)• (assert (stack $?rest1))• (assert (stack ?block1 ?block2 $?rest2))• (printout t ?block1 " moved on top of "• ?block2 "." crlf)) 26

• (defrule move-to-floor• ?goal <- (goal (move ?block1) (on-top-of floor))• ?stack-1 <- (stack ?block1 $?rest)• =>• (retract ?goal ?stack-1)• (assert (stack ?block1))• (assert (stack $?rest))• (printout t ?block1 " moved on top of floor."

crlf))

• (defrule clear-upper-block • (goal (move ?block1))• (stack ?top $? ?block1 $?)• =>• (assert (goal (move ?top) (on-top-of floor))))

• (defrule clear-lower-block• (goal (on-top-of ?block1))• (stack ?top $? ?block1 $?)• =>• (assert (goal (move ?top) (on-top-of floor))))

27

Advantages of Expert Systems

• Increased availability

• Reduced cost

• Reduced danger

• Performance

• Multiple expertise

• Increased reliability

28

Advantages Continued

• Explanation

• Fast response

• Steady, unemotional, and complete responses at all times

• Intelligent tutor

• Intelligent database

29

Problems with Algorithmic Solutions

• Conventional computer programs generally solve problems having algorithmic solutions.

• Algorithmic languages include C, Java, and C#.

• Classic AI languages include LISP and PROLOG.

30

Limitations of Expert Systems

• Typical expert systems cannot generalize through analogy to reason about new situations in the way people can.

• A knowledge acquisition bottleneck results from the time-consuming and labor intensive task of building an expert system.

31

Chapter 11. Rule-Based AI

32

Real-time strategy simulation game

• Keep track of the player's current state of technology so that the computer opponent can plan and deploy offensive and defensive resources accordingly

• Send out scouts to collect information and then make inferences given the information as it is received

33

Real-time strategy

simulation game

34

Martial arts fighting game

• Anticipate the player's next strike so that the computer opponent can make the appropriate countermove, such as a counter strike, a dodge, or a parry

• For example, if during the fight the player throws a punch, punch combination, what will the player most likely throw next: a punch, a low kick, or a high kick?

35

11.1 Rule-Based System Basics

• Working memory– stores known facts and assertions made by the

rules • Rules memory – stores known facts and assertions made by the

rules • As rules are triggered, or fired in rule-based

system lingo, they can trigger some action or state change

36

Example working memory

• enum TMemoryValue{Yes, No, Maybe, Unknown};

• TMemoryValue Peasants; • TMemoryValue Woodcutter;• TMemoryValue Stonemason;• TMemoryValue Blacksmith;• TMemoryValue Barracks; • …

37

Making rules

• The computer can gather facts on the player's current state of technology by sending out scouts and making observations

if(Woodcutter == Yes && Stonemason == Yes && Temple == Unknown)

Temple = Maybe;

38

Example priest rule

if(Priest == Yes) { Temple = Yes; Barracks = Yes; Woodcutter= Yes; Stonemason= Yes;

}

39

• Write such rules and execute them continuously during the game to maintain an up-to-date picture of the computer opponent's view of the player's technology capabilities

• Use this knowledge in other AI subsystems to decide how to deploy its attack forces and defenses.

40

Forward Chaining

1. matching rules to facts stored in working memory

2. Conflict resolution – more than one rule can match a given set of

facts in working memory – matching rule, random, highest weight

3. Fire the rule• The whole process is repeated until no more

rules can be fired

41

Conflict resolution

• Refractotiness– A rule should not be allowed to fire more than

once on the same data

• Recency• Specificity

42

Backward Chaining

• Start with some outcome, or goal, and we try to figure out which rules must be fired to arrive at that outcome or goal

if(Blacksmith == Yes) Cavalry =Yes

43

11.2 Fighting Game Strike Prediction

• Predict a human opponent's next strike in a martial arts fighting game – punch, low kick, or high kick – 27 rules to capture all possible three-strike

combinations

44

Working memory

enum TStrikes {Punch, LowKick, HighKick, Unknown};

struct TWorkingMemory { TStrikes strikeA; // previous, previous strike (data) TStrikes strikeB; // previous strike (data) TStrikes strikeC; // next, predicted, strike (assertion)

};

45

Rules

class TRule { public: TRule(); void SetRule(TStrikes A, TStrikes B, TStrikes C); TStrikes antecedentA; TStrikes antecedentB; TStrikes consequentC;

bool matched; int weight;

}; 46

27 Rules

• void TForm1::Initialize(void) { – Rules[0].SetRule(Punch, Punch, Punch);– Rules[1].SetRule(Punch, Punch, LowKick); – Rules[2].SetRule(Punch, Punch, HighKick); – Rules[3].SetRule(Punch, LowKick, Punch); – Rules[4].SetRule(Punch, LowKick, LowKick); – Rules[5].SetRule(Punch, LowKick, HighKick); – Rules[6].SetRule(Punch, HighKick, Punch);– …

47

Strike Prediction

1. Populates the working memory 1. collect some data from the player

2. Processing the previous prediction– Reinforce the matched rule by increasing the rule's

weight(meta-knowledge)3. Find the rules that match the facts stored in

working memory, conflict resolution(most weighted)

• Experiments saw success rates from 33% up to 65-80%

48