1. IT-2702 Kunstig intelligens - høst 2004 Forelesning 5. Emner: Kunnskapsintensiv problemløsning - ekspertsystemer Regelbaserte systemer Modellbaserte systemer Kunnskapsakkvisisjon og -modellering Casebaserte systemer

2. Expert system = Knowledge-based decision support system A knowledge-based computer program designed to model the problem-solving ability of a human expert. An expert system performs like a human expert within a limited domain. - Knowledge is acquired from various sources (e.g., primarily a human expert, but also books, reports, drawings, visual inspections). - Expert systems do not (necessarily) attempt to simulate human mental architecture, but to emulate human expert performance.

3. RULE BASED SYSTEMS EXPERT Rule-based systems were the first expert systems. For historical reasons, rule-based systems and expert systems are sometimes used as synonyms - and to some extent also in Luger’s book, unfortunately. The term expert system now actually covers model-based and case-based methods as well - and this is the view we will stick to in this course.

4. Kontroll-kunnskap Heuristiske regler Spesifikke case Dyp kunnskap KUNNSKAPSBASERTE METODER - Kunnskapstyper

6. Guidelines to determine whether a problem is appropriate for expert system solution: 1.The need for the solution justifies the cost and effort of building an expert system. 2.Human expertise is not available in all situations where it is needed. 3.The problem may be solved using symbolic reasoning. 4.The problem domain is well structured and does not require commonsense reasoning. 5.The problem may not be solved using traditional computing methods. 6.Cooperative and articulate experts exist. 7.The problem is of proper size and scope.

7. Important aspects of expert systems : - separation of control from knowledge. - modularity of knowledge - ease of expansion - ability of explanation - utilization of heuristic knowledge - utilization of uncertain knowledge

8. Figure 7.1: Architecture of a typical expert system for a particular problem domain.

10. The Knowledge Engineering Process - The main players on an expert system project are the domain expert, the knowledge engineer, and the end user. -Knowledge engineer designs, builds, and tests the expert system. - The major tasks of an knowledge engineer: - selecting the software and hardware tools - knowledge acquisition - organisation of this knowledge - problem-solving method identification - coding the system - testing the sytem - Domain expert possess the skill and knowledge to solve a specific problem in a manner superior the others. - End user:The final expert system should meet the needs of the end user. These needs concern: - user interface - level of explanation - information entry - form of final results

11. - Expert System development, is a highly iterative process. -The designer partially builds the system, tests it, then modifies the system's knowledge. - This process is repeated throughout the project where the system's knowledge grow with each test. Phases in expert sytem development - Exploratory developmnet cycle: Assesment Knowledge acquisition Design & Imp. Documentation Maintenance Test Refinements Explorations Requirements Knowledge Structure Evaluation Product Learning

12. Assesment: - determination of feasibility and justification of the candidate problem. - definition of the overall goal of the project. - specification of important features and the scope of the project. - establishment of the needed resources, including project personel. Knowledge Acquisition: - ’extraction’ of knowledge from the domain expert, analysis and modelling of the knowledge Design: - methods for processing the knowledge is determined. - a software tool is chosen to represent and reason with the sytem's knowledge - design of user interface - an initial prototype is built. - most often a constructive process, in which the domain expert and nowledge eningeer cooperate

13. Testing: - this is not a separate phase, but rather a continual process throughout the project. - each time new knowledge is added to the sytem, the system is tested. - the major objective of testing is to validate the overall structure of the system and its knowledge. - studies the acceptability of the system by the user. Documentation - all the project's knowledge is documented such as to meet the requirements of both the user and the developer of the system.

14. Conceptual models and their role in Knowledge Acquisition: - the knowledge of domain expert is often vague and incomplete - the knowledge engineer translates this knowledge into a formal language - knowledge acquision is the bottlenect of expert system development because: - human expertise is often not explicitely retrievable, - human expertise has often the form of knowing how, rather than knowing what, - human expertise is subjective - expertise changes. - is not a formal or executable model - is a bridge between human expertise and its implementation, serves an intermediate role in formalization of knowledge - is a knowledge level model of the systems and its interaction with the world - Conceptual model

15. The old view of building an expert system.

16. Figure 7.4: The role of mental or conceptual models in problem solving.

18. A Conceptual Model is a Knowledge Level model of the systems and its interaction with the world

20. A growing number of Knowledge Modelling Libraries are currently being developed

21. Examples of knowledge modelling methodologies coupled with libraries of knowledge modelling components 1)

23. 2)

24. 3) - goal/subgoals - the “what” - knowledge needed by methods to accomplish tasks (achieve goals) - problem solving steps - the “how”

26. Rule-based systems Example: A small rule-based expert system for analysis of automotive problems. - Production system - Goal driven - User querying

27. Figure 7.5: The production system at the start of a consultation in the car diagnostic example.

28. Figure 7.6: The production system after Rule 1 has fired.

29. Figure 7.7: The system after Rule 4 has fired. Note the stack-based approach to goal reduction.

30. Figure 7.8:The and/or graph searched in the car diagnosis example, with the conclusion of Rule 4 matching the first premise of Rule 1.

31. The following dialogue begins with the computer asking the user about the goals present in working memory.

32. Figure 7.9: The production system at the start of a consultation for data-driven reasoning. Rule-based systems - Data-driven reasoning

33. Figure 7.10: The production system after evaluating the first premise of Rule 2, which then fails. Figure 7.11: The data-driven production system after considering Rule 4, beginning its second pass through the rules.

34. Figure 7.12:The search graph as described by the contents of working memory (WM) for the data-driven breadth-first search of the rule set of Section 7.2.1.

35. Model-Based Reasoning Reasoning: Based on ”deeper” knowledge than rules Typical models: - causal - functional - behaviourial -> a combination of several submodels Representation Different relations than rule-based’s ”if-then” relation: - taxonomical (”has-subclass”, ”has-instance”) - ”has-part” -”causes” -... Often multiple relations combined!

36. Figure 7.13: The behavior description of an adder, after Davis and Hamscher (1988).

37. Figure 7.14: Taking advantage of direction of information flow, after Davis and Hamscher (1988).

38. Figure 7.15: A schematic of the simplified Livingstone propulsion system, from Williams and Nayak (1996).

39. Figure 7.16: A model-based configuration management system, from Williams and Nayak (1996).

43. Kontroll-kunnskap Heuristiske regler Spesifikke case Dyp kunnskap KUNNSKAPSBASERTE METODER - UTVIKLINGSTRENDER Integrerte systemer(f.eks. SOAR, CREEK, META-AQUA) - totalarkitekturer for intelligent problemløsning