17/1/1 © pearson education limited 2002 artificial intelligence & expert systems lecture 1 ai,...

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Artificial Intelligence & Expert Systems

Lecture 1AI, Decision Support,

Architecture of expert systems

Topic 17

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Artificial intelligence

• Emulating human thought processes• Making a computer based system behave in the

same way as a human• Applications

– natural language processing - communicate with computer using English-like statements

– expert systems, decision support systems– neural networks– retinal scanning

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Life on Mars?!• Evidence of intelligence

– traffic managementBasic system

Not automated

Intelligence High?

Sophisticated system

Automated

Intelligence Low?

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Expert Systems

• Represent the knowledge & decision making skills of experts

• Encapsulate the knowledge of experts• Provide the tools for acquisition of knowledge• Examples

– medical diagnosis, legal advice, risk assessment (all require reasoning)

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Types of Decision

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Traditional vs Expert Systems

• Traditional– calculations on data– storage and retrieval of records– credits and debits– orders/deliveries/invoices

• Expert– medical diagnosis– legal advice

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Decision Support Systems

• Degree of structure in problems– 1 the data– 2 the problem-solving procedures– 3 the goals and constraints– 4 the flexibility of strategies among the procedures

• If problem exhibits all four (e.g. credit)– operational system, procedural logic

• If problem is type 3– classic expert system solution

• Others - hybrid solution

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Role of Expert

• Body of knowledge• Apply, often with incomplete information• Deliver solution, with explanation/justification• Inform debate, identify own limitations• Interact with people requiring expertise• Improve knowledge/expertise by learning

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What is an Expert System?

• Knowledge base• Separate knowledge from particular case• Separate knowledge from inference• Interactive user interface• Output = advice and decisions

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Domain-specific Knowledge Base

• Common-sense knowledge – moral, social attitudes, individual interests

• Procedural knowledge– e.g. Recipes - do this, do that until…

• Declarative knowledge– e.g. Regulations - if this, then that, unless...– No implied order to finding the solution

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Architecture of typical expert system

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Knowledge-acquisition subsystem

• Entering the domain-specific knowledge• Can enter rules directly (next week’s task)• Often accomplished using an expert system

shell• Analogous to rote learning• Compare to scientific discovery…

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Case-specific Knowledge Base

• facts specific to the particular situation• entered by keyboard...• or taken from external database…• or derived from knowledge base…• or gleaned from experience

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Inference Engine

• Apply domain-specific knowledge to particular facts of the situation to derive new conclusions

• Sound inference principles– modus ponens

• rule If it is raining then the ground is wet

• fact It is raining• derived new fact The ground is wet

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Inference Engine

• Sound inference principles– modus tollens

• rule If it is raining then the ground is wet

• fact The ground is not wet• derived new fact It is not raining

• Need a sound inference control strategy– which rules to apply (tutorial exercise)

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Knowledge base

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Explanation Subsystem

• ‘How’ questions– how was the conclusion reached– intermediate solutions

• ‘Why’ questions– why was a piece of information required

• Further explanation– what do you mean by…

• Consultation Trace

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Simple expert system

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Simple expert system

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Practical activities

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