1. Intelligent Control Methods Lecture 5: Expert Systems Slovak University of Technology Faculty of Material Science and Technology in Trnava

2. 2 Briefly from the history (recapitulation): Initially: Universal problem solvers, without orientation to problem  inefficient 70th years: Universal problem solvers for concrete problems Since 80th years: Special knowledge for concrete tasks solution

3. 3 Briefly from the history (recapitulation): AI-system quality Universal solution methods for universal problems Universal solution methods for concrete problems Specialized knowledge about concrete problem area 1950 1960 19701980 1990 2000

4. 4 AI-system is able to solve intelectual tasks, if it has knowledge about problem area. Result of this principle: Expert systems ES: Program systems, which use expert knowledge for problem solution in narrow problem area

5. 5 Place of ES in AI-systems: AI-systems Knowledge systems ES

6. 6 Technology of ES-design: Knowledge engineering Expert (problem area) Knowledge engineer (IT) ES Questions, tasks Answers, solutions

7. 7 ES-attributes: Core of ES: Knowledge-base  module separated from inference  knowledge formulated explicitly  sizeable (2000 – 3000 units) Solutions on expert level  creative, exact, fast, effective Explanatory competence Prognostic possibilities Institutional memory Possibilities for experts teaching

8. 8 ES Architecture: User Communication module Inference module Explanatory module DB (facts about problem KB (knowledge about problem area) SHELL

9. 9 ES advantages (compared with people experts): ES-knowledge are permanent  People forget, are tired and moody ES-knowledge can be easy transferred  Copying, not study ES-knowledge can be easy documented  In contrast to people knowledge they are explicitly represented by data structures ES-knowledge can be used in many places in the same time

10. 10 People experts advantages (compared with ES): People are creative  They reorganize information, master unexpected situations People learn People receive information by sense  ES: only texts, sometimes by sensors People reason in context  In KB of ES is only knowledge from problem area

11. 11 Implementation of ES is proper, when: The task is solvable by phone, Experts are disposable and The solution takes time 3 min. – 3 ours.

12. 12 Implementation of ES is proper, when: Experts are available and the experts are able to describe their knowledge and solution of various experts are not opposite and the solved problem needs only specific, not general knowledge and the problem needs only intellectual, not physical givennesses and the task is not new and the task is not easy or extremely difficult and

13. 13 Implementation of ES is proper, when: The solution brings profit or experts are not available or expensive or they are needed in more places in the same time or knowledge are needed after expert outgoing or the system should work in dangerous conditions.

14. 14 Main application areas of ES: Interpretation (data analysis with the goal to determine the state)  Data can be uncertain, incorrect, incomplete...)  Used in chemistry, geology, health,... Prognosis (determination of the current state development)  Incomplete information  Different alternatives conditioned by next events  Prices, harvest,...

15. 15 Main application areas of ES: Diagnostics (technical or medical)  Determination of malfunction (illness) by symptoms  In a broad the same like classification Design (documentation creating needed for object implementation)  Electronic circuits (computers VAX)  Different alternatives by concrete conditions  Design of complex units as a set of simple ones (modularization)

16. 16 Main application areas of ES: Planning (activities design)  ES TATR – planning of flying attack  Activities depends on conditions, which will be known later  Many possibilities Monitoring (observation of the actual state and its comparison with expected one)  Real time,  Nuclear reactors.

17. 17 Main application areas of ES (principally): Diagnostic ES: solution alternatives exist ahead, ES only select the best ones (interpretation, prognosis, diagnostics, monitoring) Generative ES: solution originates during the solution process (planning, design)