METHODs OF CONTROL STRATEGIES in expert systems
LIST AND EXPLAIN THE SIX METHOD OF CONTROL STRATEGIES.
An expert system is a computer program that provides expert-level solutions to important problems and is:
➢ heuristic - It reasons with judgmental knowledge as well as with formal knowledge of established theories
➢ transparent -It provides explanations of its line of reasoning and answers to queries about its knowledge
➢ flexible -It integrates new knowledge incrementally into its existing store of knowledge.
By definition, An expert system is a computer program that simulates the thought process of a human expert to solve complex decision problems in a specific domain.
An expert system operates as an interactive system that responds to questions, asks for clarification, makes recommendations, and generally aids the decision-making process. Expert systems provide expert advice and guidance in a wide variety of activities, from computer diagnosis to delicate medical surgery.
An expert system is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision problems based on knowledge acquired from an expert.
Moreso, Expert systems are typically very domain specific. For example, a diagnostic expert system for troubleshooting computers must actually perform all the necessary data manipulation as a human expert would. The developer of such a system must limit his or her scope of the system to just what is needed to solve the target problem. Special tools or programming languages are often needed to accomplish the specific objectives of the system.
The Need for Expert Systems
Expert systems are necessitated by the limitations associated with conventional human decision-making processes, including: 1. Human expertise is very scarce.
2. Humans get tired from physical or mental workload.
3. Humans forget crucial details of a problem.
4. Humans are inconsistent in their day-to-day decisions.
5. Humans have limited working memory.
6. Humans are unable to comprehend large amounts of data quickly. 7. Humans are unable to retain large amounts of data in memory. 8. Humans are slow in recalling information stored in memory. 9. Humans are subject to deliberate or inadvertent bias in their actions. 10. Humans lie, hide, and die.
11. Humans can deliberately avoid decision responsibilities.
Benefits of Expert Systems
Expert systems offer an environment where the good capabilities of humans and the power of computers can be incorporated to overcome many of the limitations discussed in the previous section. Expert systems: 1. Increase the probability, frequency, and consistency of making good decisions 2. Help distribute human expertise
3. Facilitate real-time, low-cost expert-level decisions by the non-expert 4. Enhance the utilization of most of the available data
5. Permit objectivity by weighing evidence without bias and without regard for the user’s personal and emotional reactions 6. Permit dynamism through modularity of structure
7. Free up the mind and time of the human expert to enable him or her to concentrate on more creative activities 8. Encourage investigations into the subtle areas of a problem
The control strategies are:
(1) Data - Driven (Forward chaining) Control Strategy
(2) Goal - Driven (Backward Chaining) Control Strategy
(3) Bottom-Up Control Strategies
(4) Model -Based Control Strategy (Top-Down Control Strategy) (5) Non-Hierarchical Control Strategy
(6) Combined / Mixed Control Strategies
1. Data-Driven Control
This method involves checking the condition part of a rule to determine whether it is true or false. If the condition is true, then the action part of the rule is also true. This procedure continues until a solution is found or a dead end is reached. Data-Driven reasoning is commonly referred to as Forward chaining.
References:  Tang Renyuan, Liu Qingrui, Qian Fanghua, Tang Hai. An expert system for the design of permanent magnet sychronous motors. Proceedings of China International Conference on Electrical Machines, september. pp. 18–20, 1991.
 Feng Xinhua, Fan Jianshan. The expert system for designing 3-phase medium-induction motors. Proceedings of China International Conference on Electrical Machines, september. pp. 18–20, 1991.
 Aikins, J. S. Prototvpes and Production Rules: A Knowledqe Representation Q Computer Consultations. Doctoral dissertation, Heuristic Programming Project, Dept. of Computer Science, Stanford University, 1980.
 Erman, L. D., and Lesser, V. R. “A multi-level organization for problem- solving using many diverse cooperating sources of knowledge.” In Proc. IJCAI-75, pp. 483-490.
 Fagan, L. M. VM: Representinq Time-Deoendent Relations in a Medical Settmq. Doctoral dissertation, Heuristic Programming Project, Dept. of Computer Science, Stanford University, 1980.
 Kunz, J., Fallet, R., McClung, D., Osborn, J., Votteri, B., Nii, H. P., Aikins, J. S., Fagan, L., and Feigenbaum, E. “A Physiological Rule Based System for Interpreting Pulmonary Function Test Results.” HPP-78.19
 (Working Paper), Heuristic Programming Project, Dept. of Computer Science, Stanford University, December 1978.
 Nii, H. P., and Aiello, N. “AGE: A knowledge-based program for building knowledge-based programs”. In Proc of IJCAI-79, Tokyo, Japan, August, 1979, pp. 645655.
 Van Melle, W. EMYCIN. A Domain-independent Production-rule System for Consultation Programs, Doctoral dissertation, Heuristic Programming
 Project, Dept. of Computer Science, Stanford University, 1980.
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