1. Introduction to Rule-Based Systems, Expert Systems, Fuzzy Systems Introduction to Rule-Based Systems, Expert Systems, Fuzzy Systems (sections 2.7, 2.8, pp. 118-132; and partially 3.1 and 3.4) N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

2. Sub-topics: n Production rules and production systems n How to program in rules? n Advantages and limitations of the production systems n Expert systems n Fuzzy sets n Fuzzy rules and fuzzy inference n Fuzzy information retrieval and fuzzy databases n Fuzzy expert systems N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

3. Production Rules and Production Systems n A production rule consists of two parts: condition (antecedent) part and conclusion (action, consequent) part, n i.e: IF (conditions) THEN (actions) n Example IF Gauge is OK AND [TEMPERATURE] > 120 THEN Cooling system is in the state of overheating N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

4. Production Rules and Production Systems... n This rule consists of 2 propositions given on separate lines (2 condition elements) and a conclusion. The second condition element contains a variable. Condition elements in a rule can be connected by different connectives, the most used being AND, OR, NOT. N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

5. Production Rules and Production Systems... n A production system consists of: Working memory (facts memory) Production rules memory Inference engine, it cycles through three steps: – match facts against rules –select a rule –execute the rule N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

6. Production Rules and Production Systems... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n Figure 2.25: A production system cycle

7. How to Program in Production Rules? N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n Figure 2.27: A program written in a production language for the family relationship problem

8. Advantages and Limitations of the Production Systems (PS): n PS are universal computational mechanism n PS are universal function approximators n readability n explanation n expressiveness n modularity N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

9. Expert Systems N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n They are information systems for solving a specific problem which provides an expertise similar to those of experts in the problem area. n An ES contains expert knowledge.

10. Expert Systems... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n A typical ES architecture consists of: knowledge base module working memory module (for the current data) inference engine forward chaining (inductive, data driven) backward chaining (deductive, goal driven) user interface (possibly a NLI, menu, windows, etc) explanation module

11. Expert Systems... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n Figure 2.29: An expert system architecture

12. Expert Systems... n `How' and `Why' explanations in ES N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n Figure 2.30 HOW and WHY explanation for The Car Monitoring Production System

13. Data Analysis... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n Expert systems design identification conceptualization formalization realization validation n The knowledge acquisition problem: interview experts learning from data literature agents on the Web

14. Fuzzy Sets N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n Figure 3.1 Membership functions representing three fuzzy sets for the variable "height".

15. Fuzzy Sets... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n Figure 3.2 Representin g crisp and fuzzy sets as subsets of a domain (universe) U

16. Fuzzy Sets... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n n Figure 3.3 Support of a fuzzy set A n see also fig 3.21 for an example of fuzzy sets definitions for the The Bank Loan Decision problem.

17. Fuzzy Rules and Fuzzy Inference N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n Rule 1: IF (CScore is high) and (CRatio is good) and (CCredit is good) then (Decision is approve) n Rule 2: IF (CScore is low) and (CRatio is bad) or (CCredit is bad) then (Decision is disapprove)

18. Fuzzy Rules and Fuzzy Inference... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n Inputs to a fuzzy system can be: fuzzy, e.g. (Score = Moderate), defined by membership functions exact, e.g.: (Score = 190); (Theta = 35), defined by crisp values. n Outputs from a fuzzy system can be: fuzzy, i.e. a whole membership function, or exact, i.e. a single value is produced on the output.

19. Fuzzy Rules and Fuzzy Inference... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n Fuzzy inference methods: n `Fuzzification- rule evaluation- defuzzification' inference n n see Figure 3.27 for an illustration of "crisp input data rules evaluation defuzzification" inference for a particular crisp input data for the Bank Loan Decision system.

20. Fuzzy Rules and Fuzzy Inference... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n Methods for defuzzification: center of gravity mean of maxima n n see Figure 3.26 Methods of defuzzification: the centre of gravity method (COG), and the mean of maxima method (MOM) applied over the same membership function for a fuzzy output variable y. They calculate different crisp output values.

21. Fuzzy Information Retrieval and Fuzzy Databases N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n Fuzzy interfaces to standard databases (see fig 3.32) n Fuzzy databases (see fig. 3.33) n Fuzzy expert system shells (see fig. 3.36, 3.37)

22. Fuzzy Information Retrieval and Fuzzy Databases... N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996 n Fuzzy expert systems n n Figure 3.35: A block diagram of a fuzzy expert system.

23. Fuzzy Expert Systems n Fuzzy systems are: easy to develop and debug easy to understand easy and cheap to maintain N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996