ross@PESCADERO.STANFORD.EDU.UUCP (01/29/87)
CS 500 Computer Science Colloquium Feb. 3, 4:15 pm, Skilling Auditorium THE PROVISION OF INDUCTION AS A PROBLEM SOLVING METHOD IN MODEL BASED SYSTEMS DAVID HARTZBAND, D.Sc. Artificial Intelligence Technology Group Digital Equipment Corporation, Hudson, MA Much research in artificial intelligence and cognitive science has focused on mental modeling and the mapping of mental models to machine systems. This is especially critical in systems which provide inference capabilities in order to enhance peoples' problem solving abilities. Such a system should present a machine model that is homomorphic with a human perception of knowledge representation and problem solving. An approach to the development of such a model has allowed a model-theoretic approach to be taken toward machine representation and problem solving. Considerable work done in psychology, cognitive science and decision analysis in the past 20 years has indicated that human problem solving methods are primarily comparative (that is analogic) and proceed by successive refinement of comparisons among known and unknown entities (e.g. Carbonell, 1985; Rummelhart and Abrahamson, 1973; Simon, 1985; Tversky, 1977). A series of algorithms has been developed to provide analogic (Hartzband et al. 1986) and symmetric comparative induction methods (Hartzband and Holly, in preparation) in the context of the homomorphic machine model previously referred to. These general methods can be combined with heuristics and structural information in a specific domain to provide a powerful problem solving paradigm which could enhance human problem solving capabilities. This paper will: a. describe the characteristics of this model-theoretic approach, b. describe (in part) the model used in this work, c. develop both the theory and algorithms for comparative induction in this context, and d. discuss the use of these inductive methods in the provision of effective problem solving paradigms.