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Chapter 7

King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems. Chapter 7. Expert Systems Part I. What is an Expert System?.

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Chapter 7

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  1. King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems Chapter 7 Expert Systems Part I

  2. 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

  3. Introduction Two main approaches for problem solving using knowledge: • Symbolic Approach • Expert systems: Feed the system with knowledge. There is no learning !! • Sub-symbolic approaches • Machine learning:the construction and study of systems that can learn from data. Neural Networks learns from examples by themselves.

  4. Symbolic Approach

  5. Expert system • Expert system: a rule based program which encapsulates knowledge from some domain, normally obtained from a human expert in that domain • An expert system simulates a human expert in his/her field of expertise in an attempt to solve a particular problem. • Expert Systems do not replace experts, but they • make their knowledge and experience more widely available • permit non-experts to work better • Expert System acts as a Consultant or Advisor

  6. Expert Systems Objective and Activities of an Expert System • Objective of an expert system • To transfer expertise from an expert to a computer system and • Then on to other humans (non experts) • Activities • Knowledge acquisition • Knowledge representation • Knowledge inferencing • Knowledge transfer to the user • Knowledge is stored in a knowledge base

  7. Architecture of an Expert System

  8. User Interface Inference Engine Knowledge Base Structure of an Expert System Major Components • Knowledge base (KB): repository of rules, facts (productions) • working memory: (if forward chaining used) • inference engine: the deduction system used to infer results from user input and KB • user interface: interfaces with user

  9. User Interface Inference Engine Knowledge Base Structure of an Expert System Knowledge base • The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems • Two Basic Knowledge Base Elements • Facts • Production rulesthat direct the use of knowledge • Production rules can be expressed in IF-THEN pseudocode format. • Knowledge is the primary raw material of an Expert System • Incorporated knowledge representation

  10. User Interface Inference Engine Knowledge Base Structure of an Expert System Inference Engine • The brain of the ES • The control structure (rule interpreter) • Provides methodology for reasoning • Inference Engine performs inferences.

  11. User Interface Inference Engine Knowledge Base Structure of an Expert System User Interface • Mechanism by which user and system communicate. • Language processor for friendly, problem-oriented communication • Natural Language Processing, or menus and graphics

  12. User Interface Inference Engine Knowledge Base Structure of an Expert System Working Memory (Blackboard) • Global database that contains • facts used by rules • Intermediate results • Records Intermediate Hypotheses and Decisions • Agenda • Solution

  13. User Interface Inference Engine Knowledge Base Structure of an Expert System Explanation Subsystem (Justifier) • Explains reasoning of expert system to user by interactively answering questions: • Why? • How? • What? • (Where? When? Who?)

  14. Developing Expert Systems • The Human Element in Expert Systems • Expert • Knowledge Engineer • User • Others

  15. Developing Expert Systems • Expert • Has the special knowledge, judgment, experience and methods to give advice and solve problems • Provides knowledge about task performance

  16. Developing Expert Systems • Knowledge Engineer • Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counterexamples, and bringing to light conceptual difficulties • Usually also the System Builder

  17. Developing Expert Systems • User • Possible Classes of Users • A non-expert client seeking direct advice (ES acts as a Consultant or Advisor) • A student who wants to learn (Instructor) • An ES builder improving or increasing the knowledge base (Partner) • An expert (Colleague or Assistant) • The Expert and the Knowledge Engineer Should Anticipate Users' Needs and Limitations When Designing ES

  18. Knowledge base: production system • Knowledge is represented using rules of the form: • Rule: if Conditions then Conclusions or • Rule: if Premises then Actions or • Rule: if if-partthen then-part • A rule as described above is often referred to as a production rule.

  19. Knowledge base: production system Examples: • if symptom1 and symptom2 and symptom3 then disease1 • if - the leaves are dry, brittle and discoloured then - the plant has been attacked by red spider mite • If – it is raining then – you should take an umbrella • if - the customer closes the account then - delete the customer from the database

  20. Inference engine: rule-based reasoning • The essence of a rule-based reasoning system is that it goes through a series of cycles. • In each cycle, it attempts to pick an appropriate rule from its collection of rules, depending on the present circumstances, and to use it. • Because using a rule produces new information, it's possible for each new cycle to take the reasoning process further than the cycle before. This is rather like a human following a chain of ideas in order to come to a conclusion.

  21. Inference engine: Forward Chaining • Forward Chaining is based on Modus Ponens inference rule: (A, A  B ) / B • In other words, if A is true and we have A  B then we can deduce that B is true • In the context of Expert System, it is used as follows: • if A is in WM and we have a rule in KB of the form if A then B then we can deduce B (add B to WM as new information)

  22. Inference engine: Forward Chaining Do until problem is solved or no antecedents match Collect the rules whose antecedents are found in WM (create the Agenda). If more than one rule matches use conflict resolution strategy to eliminate all but one Do actions indicated in by rule “fired” (i.e. perform conclusion part of rule) • Agenda: a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory.

  23. Matching Rules filtering Rules Conflict Resolution Rule Execution Apply the rule Add Then-Part to WM Inference engine: Forward Chaining For Conflict Resolution we can use rule-order as an implied priority Cycles

  24. Matching Rules filtering Rules Conflict Resolution Rule Execution Apply the rule Add Then-Part to WM Inference engine: Forward Chaining Algorithm: • Match WM with KB to select production rules • Eliminate already applied rules • If many rules select one which has the smallest number • Apply selected rule by adding its conclusion to WM • If the problem is solved or no new information added then stop otherwise go to step 1

  25. Inference engine: Forward Chaining Inference Engine: Execute many cycles Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B

  26. Inference engine: Forward Chaining Inference Engine: Execute many cycles Cycle 1: Rule 5→ add C Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C

  27. Inference engine: Forward Chaining Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5→ add D Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C,D

  28. Inference engine: Forward Chaining Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5→ add D Cycle 3: Rule 1, Rule 2, R 3, R 4, Rule 5 → add G Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C,D, G

  29. Inference engine: Forward Chaining Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5→ add D Cycle 3: Rule 1, Rule 2, R3, R 4, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C,D, G,F

  30. Inference engine: Forward Chaining Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2,Rule 5→ add D Cycle 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Cycle 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C,D, G,F,E

  31. Inference engine: Forward Chaining Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5→ add D Cycle 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Cycle 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6→ add E Cycle 6: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add Z Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C,D, G,F,E,Z

  32. Inference engine: Forward Chaining Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5 → add D Cycle 3: Rule 1, Rule 2, Rule 3,Rule 4, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Cycle 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E Cycle 6: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add Z Cycle 7: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → no rule => STOP !!! Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C,D, G,F,E,Z

  33. Inference engine: Backward Chaining • Backward Chaining is also based on Modus Ponens inference rule; • In the context of Expert System, it is used as follows: • To prove a goal B which is not in the WM and if we have a rule in KB of the form if A then B then we just have to prove A (A is either present in WM or it exists as a rule with A as a conclusion)

  34. Inference engine: Backward Chaining • Consider the following KB and WM: Inference Engine: Exploration of AND/OR Tree: Suppose we have the goal: G To prove G, one can use Rule 2 Proving G is equivalent of proving B and C As B is present in the WM so it’s true So remains the proof of C, one can use Rule 5 Proving C is equivalent of proving A and B As A and B are present in WM (true), we can deduce that C is true B and C are true then G is true Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B

  35. Expert System Shells • Shell = Inference Engine + User Interface • ESS allow non programmers to build an expert system, by inserting facts and rules into a generic expert system architecture which is already built. • Some ESSs • BABYLON • JESS • ES • CLIPS…..

  36. Advantages of Expert Systems • An expert system can operate constantly 24 hours per day. • An expert system can exceed the performance of any human expert, as it can combine knowledge from a number of different experts.

  37. Limitations of Expert Systems • Not able to cope with unseen information. • Not able to cope with noise. • Not able to adapt to new environments. • Not able to learn independently in a similar manner that humans learn. They need to be programmed in advance.

  38. Conclusions • Why use expert systems: • commercial viability: whereas there may be only a few experts whose time is expensive and rare, you can have many expert systems • expert systems can be used anywhere, anytime • expert systems can explain their line of reasoning • commercially beneficial: the first commercial product of AI • Weaknesses: • expert systems are as sound as their KB; errors in rules mean errors in diagnoses • automatic error correction, learning is difficult • the extraction of knowledge from an expert, and encoding it into machine-inferable form is the most difficult part of expert system implementation

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