17/1/1 © pearson education limited 2002 artificial intelligence & expert systems lecture 1 ai,...
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17/1/3 © Pearson Education Limited 2002 Life on Mars?! Evidence of intelligence –traffic management Basic system Not automated Intelligence High? Sophisticated system Automated Intelligence Low?TRANSCRIPT
© Pearson Education Limited 2002 17/1/1
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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