Fuzzy Set
130:
Rule-based Expert Systems
Ajith Abraham
Oklahoma State University, Stillwater, OK, USA
1 Problem Solving Using Heuristics 2 What are Rule-based Systems? 3 Inference Engine in Rule-based Systems 4 Expert System Development 5 Fuzzy Expert Systems 6 Modeling Fuzzy Expert Systems 7 Illustration of Fuzzy Expert System Design 8 Adaptation of Fuzzy Inference Systems 9 Summary References
909 910 911 911 912 914 914 918 918 919
1 PROBLEM SOLVING USING HEURISTICS
A general introduction to artificial intelligence methods of measurement signal processing is given in Article 128, Nature and Scope of AI Techniques, Volume 2. Problem solving is the process of finding a solution when the path leading to that solution is uncertain. Even though we are familiar with several problem-solving techniques, in the real world, sometimes many problems cannot be solved by a technique we are familiar with. Surprisingly, for some complicated problems, no straightforward solution technique is known at all. For these problems, heuristic solution techniques may be the only alternative. A heuristic can be simplified as a strategy that is powerful and general, but not absolutely guaranteed to provide best solutions. Heuristic methods are very problem specific. Previous experience and some general rules – often called rules of
thumb – could help find good heuristics easier. Humans use heuristics a great deal in their problem solving. Of course, if the heuristic does fail, it is necessary for the problem solver to either pick another heuristic, or know that it is appropriate to give up. Choosing random solutions, adopting greedy approaches, evolving the basic heuristics for finding better heuristics are just some of the popular approaches used in heuristic problem solving (Michalewicz and Fogel, 1999). Heuristic problem solving involves finding a set of rules, or a procedure, that finds satisfactory solutions to a specific problem. A good example is finding one’s way through a maze. To make the way
Rule-based Expert Systems
Ajith Abraham
Oklahoma State University, Stillwater, OK, USA
1 Problem Solving Using Heuristics 2 What are Rule-based Systems? 3 Inference Engine in Rule-based Systems 4 Expert System Development 5 Fuzzy Expert Systems 6 Modeling Fuzzy Expert Systems 7 Illustration of Fuzzy Expert System Design 8 Adaptation of Fuzzy Inference Systems 9 Summary References
909 910 911 911 912 914 914 918 918 919
1 PROBLEM SOLVING USING HEURISTICS
A general introduction to artificial intelligence methods of measurement signal processing is given in Article 128, Nature and Scope of AI Techniques, Volume 2. Problem solving is the process of finding a solution when the path leading to that solution is uncertain. Even though we are familiar with several problem-solving techniques, in the real world, sometimes many problems cannot be solved by a technique we are familiar with. Surprisingly, for some complicated problems, no straightforward solution technique is known at all. For these problems, heuristic solution techniques may be the only alternative. A heuristic can be simplified as a strategy that is powerful and general, but not absolutely guaranteed to provide best solutions. Heuristic methods are very problem specific. Previous experience and some general rules – often called rules of
thumb – could help find good heuristics easier. Humans use heuristics a great deal in their problem solving. Of course, if the heuristic does fail, it is necessary for the problem solver to either pick another heuristic, or know that it is appropriate to give up. Choosing random solutions, adopting greedy approaches, evolving the basic heuristics for finding better heuristics are just some of the popular approaches used in heuristic problem solving (Michalewicz and Fogel, 1999). Heuristic problem solving involves finding a set of rules, or a procedure, that finds satisfactory solutions to a specific problem. A good example is finding one’s way through a maze. To make the way