INFO612 Knowledge-Based Systems

Dr. R. Weber

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Expert Systems Expert Systems are the first successful

knowledge-based methodology uses knowledge (in its knowledge base) &

reasoning Systems that manipulate knowledge and reasoning

to solve problems rationally.

KBS, Knowledge engineering, ES

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Expert Systems, what and what not ES use knowledge and inference procedures to

solve problems that are difficult enough to require human expertise to solve (Feigenbaum, 82)

ES is a methodology to develop computer programs that manipulates expertise to solve problems that require human expertise in restricted domains (Weber 02)

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Expert Systems: history (i)

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Expert Systems: history (ii)

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Expert Systems: domain areas

agriculture, business, chemistry, communications, computer systems, education, electronics, engineering, environment, geology, law, manufacturing, mathematics, medicine, simulation, transportation, etc.

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Expert Systems: when

ES are indicated to solve expert problems in restricted domains without an efficient algorithmic solution

is there an alternative method? ill-structured problems is the domain well-bounded? how available is the source of knowledge? is the approach to the problem it trial-and-error? is the approach to the problem heuristic?

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Expert Systems: tasks

analysis, configuration, control, design, diagnosis, instruction, interpretation, monitoring, planning, prediction, prescription, prognosis, remedy, selection and simulation.

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ES and AI tasks

From: Durkin, J. (1994). Expert Systems: design and development. Prentice-Hall, Inc., New Jersey.

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Types of AI tasks

mundane: face recognition argumentation shopping planning

expert: diet prescription medical diagnosis legal argumentation legal, military, business planning

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the concept

knowledgebase

(e.g.,framesand methods)

knowledgebase

(e.g.,framesand methods)

expertproblemexpert

problem

inferenceengine

(agenda)

inferenceengine

(agenda) expertsolutionexpert

solution

knowledge

reasoning

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expertsolutionexpert

solution

The complete methodology

knowledgebase

(e.g.,framesand methods)

knowledgebase

(e.g.,framesand methods)

explanationexplanation

generalknowledgegeneral

knowledge

userInterface

userInterface

expertproblemexpert

probleminferenceengine

(agenda)

inferenceengine

(agenda)

working memory(short-term mem/information)

working memory(short-term mem/information)

Knowledge acquisitionKnowledge acquisition

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What are rule-based expert systems?

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Represent knowledge?

How ES represent knowledge? Knowledge representation formalisms

associated to ES:• rules• semantic networks• frames• logic

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(Production) Rules

A logic sequence of an antecedent (premise, condition) and a consequence (conclusion, action).

Both antecedent and conclusion are, in essence, facts.

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(Production) Rules (ii)

The antecedent attempts to verify if the fact is true or false, when the fact composing the antecedent is true, the conclusion is triggered.

The antecedent can be composed of several facts connected through operators such as and, or, and not.

Conclusions usually change or assign values to attributes of an object, call methods or trigger other rules.

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Semantic Networks (SN)• commonly used in logic-based expert systems• directed graphs where:

• nodes represent objects and concepts• arcs represent relationships between objects and

attributes

• Quillian, 1968• used to represent static elements of a

representation such as the class, the instances and its features

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Characteristics of SN

• cannot represent all magnitude of data (meal varying from sandwich to 20 course meal)

• very restricted in terms of inferencing• only inheritance through instance and subclass

• convenient when mathematical algorithms are applied over knowledge because graphs also provide a formal and precise representation model

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Example of Semantic Networks

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Frames

representation formalism commonly used in expert systems

represents declarative, structural and procedural knowledge

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Frames

Introduced by M. Minski in 1975, “A frame is a data-structure for representing a

stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame. Some is about what one can expect to happen next. Some is about what to do if these expectations are not confirmed”.

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Concepts, Objects and Facts

An object is a basic entity that can be instantiated.

A concept tells something about the object.

A concept can be represented as an abstraction of an object when several objects can be grouped under the same concept (e.g., client 1, client 2, all clients);

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Concepts, Objects and Facts (ii)

or a concept can be an attribute, when it tells something exclusively about this object or due to the analysis it is not worthy to represent it as an abstraction.

When an object is associated to a valued attribute, it is a fact. A fact is a statement that can be either true or false (Durkin, 1994).

Concepts can be described in a computer program via Y/N or T/F statements.

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Characteristics of frames

support inheritance (subclasses and instances)

support methodswhen neededafter changedbefore changed

easy to implement in different programming paradigms, logic-based or not

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Rules combined with frames

advantages faster inferences, increases inferential efficiency

rules with variables in its antecedents and conclusions

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Decision trees

Knowledge representation formalism Represent mutually exclusive rules (disjunction) A way of breaking up a data set into classes or

categories

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Decision treesconsist of:-leaf nodes (classes)

- decision nodes (tests on attribute values)

-from decision nodes branches grow for each possible outcome of the test

From Cawsey, 1997

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Knowledge representation formalisms

rules logic concepts, frames semantic nets decision trees

representational adequacyinferential adequacyinferential efficiency

clear syntax and semanticsnaturalness

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Inference engine forward chaining backward chaining logic theory

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Investment Advisor

Frames Concepts, Objects, Facts Rules Backward Chaining

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Heart Attack Triage

Facts/Predicates Rules Forward Chaining

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Clips

Rule-based Forward chaining Logic-based

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Clips rules or productions

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Compound productions

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Expert Systems: requirements

high quality: system must perform equally or better than a human expert

response time should be adequate to the problem it solves

reliable: not prone to crashes & errors

explanation capability should be present with the purpose of justification and verification of correctness (p. 9,10 for explanation styles)

flexible: supported by good maintenance methods

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Advantages (i) Permanence of knowledge: Expert systems do not forget or retire or quit, but human experts may

Breadth: One ES can entail knowledge learned from an unlimited number of human experts.

Reproducibility: Many copies of an expert system can be made, but training new human experts is time-consuming and expensive.

Efficiency: can increase throughput and decrease personnel costs

Differentiation: In some cases, an expert system can differentiate a product or can be related to the focus of the firm

Cost: Although expert systems are expensive to build and maintain, they are inexpensive to operate. Development and maintenance costs can be spread over many users. Cost savings, e.g., wages, minimize loan loss, reduce customer support effort. The overall cost can be quite reasonable when compared to expensive and scarce human experts

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Advantages (ii) Documentation - An expert system can provide permanent documentation of the decision process

Increased availability: the mass production of expertise

Completeness - An expert system can review all the transactions, a human expert can only review a sample; an ES solution will always be complete and deterministic

Timeliness - Fraud and/or errors can be prevented. Information is available sooner for decision making

Consistency - With expert systems similar transactions handled in the same way. Humans are influenced by recency effects and primacy effects (early information dominates the judgment).

Entry barriers - Expert systems can help a firm create entry barriers for potential competitors

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Advantages (iii)

Computer programs are best in those situations where there is a structure that is noted as previously existing or can be elicited

Reduced danger: ES can be used in any environment

Reliability: ES will keep working properly regardless of of external conditions that may cause stress to humans

Explanation: ES can trace back their reasoning providing justification, increasing the confidence that the correct decision was made

Domain analysis: Indirect advantage is that the development of an ES requires that knowledge and processes are verified for correctness, completeness, and consistency. If there is a maze of rules (e.g. tax and auditing or laws), then the expert system can "unravel" the maze

Maintenance: only knowledge base can be modified, without interference to other modules of the program

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Disadvantages of Rule-based ES (i)

Common sense - In addition to a great deal of technical knowledge, human experts have common sense. To program common sense in an ES, you must acquire and represent rules.

Creativity - Human experts can respond creatively to unusual situations, expert systems cannot.

Learning - Human experts automatically adapt to changing environments; expert systems must be explicitly updated.

Complexity and interrelations of rules grow exponentially as more rules are added.

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Degradation - Expert systems are not good at recognizing when no answer exists or when the problem is outside their area of expertise. So, ES may provide a solution that is not optimal like one that is optimal

High knowledge engineering requirements: In many real world domains, the amount of knowledge necessary to cover an expert problem is abundant making ES development time-consuming and complex

Knowledge acquisition bottleneck

Difficulty to deal with imprecision (I.e., incompleteness, , uncertainty, ignorance, ambiguity) poses an extra engineering requirement; treatments of imprecision also have to be represented

Disadvantages of Rule-based ES (ii)

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Necessary grounds for computer understanding

Ability to represent knowledge and reason with it.

Perceive equivalences and analogies between two different representations of the same entity/situation.

Learning and reorganizing new knowledge.

From Peter Jackson (1998) Introduction to Expert systems. Addison-Wesley third edition. Chapter 2, page 27.