Titlebook: Intelligent Systems for Engineering; A Knowledge-based Ap Ram D. Sriram Textbook 1997 Springer-Verlag London 1997 artificial intelligence.c

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Knowledge-Centered Problem Solving Strategies,ms search-centered methods should be augmented with: 1) domain specific knowledge, and 2) problem solving techniques to deal with: a) goals and data, b) multiple levels of abstraction, c) assumptions and justifications, d) inexactness of knowledge and data, e) time, and f ) spatial reasoning [59] (see Figure 4.1).

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Project Management,t section (Section 9.2), a brief description of COMPASS is provided. This is followed by discussions of the KBES development cycle (Section 9.3). Knowledge acquisition methodologies are addressed in Section 9.4. Issues in technology transfer are discussed in Section 9.5. Much of this discussion is based on techniques developed by Prerau [62].

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Knowledge-Based System Building Tools, does not come with a problem solving mechanism. COSMOS (C++ Object-oriented System Made fOr expert System development) was developed to address these deficiences. COSMOS is available gratis to universities and other non-profit research organizations.

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Neural Networks,k is constructed from a set of simple processing units, each capable only of a few computations such as summation and threshold logic. The power gained by a neural network is that there are many of these processors and that each processor is connected to many others, in much the same way that the neurons in our brain are highly interconnected.

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Knowledge-Centered Problem Solving Strategies,arch spaces, due to the lack of knowledge needed to guide search. To adequately exploit expert’s knowledge and to deal with complex engineering problems search-centered methods should be augmented with: 1) domain specific knowledge, and 2) problem solving techniques to deal with: a) goals and data,

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Analogical and Case-Based Reasoning, pervasive in engineering disciplines, particularly design. As there are no established, widely accepted, theories and methods for engineering design, practitioners often rely on prior design cases to exploit past successes and to avoid repeating the same mistakes. Research in analogical problem sol

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Neural Networks,nce of interest (the first surge occurred in the 1960s) as a paradigm of computation and knowledge representation. The increase in interest in this “new” area of artificial intelligence (AI) is illustrated by the number of research and application papers appearing in conferences concerning neural ne

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