Theory formation in artificial intelligence

Authors:
L.-M. Fu
Affiliations:
Department of Electrical Engineering, and Computer Science, Milwaukee, Wisconsin
Venue:
CSC '89 Proceedings of the 17th conference on ACM Annual Computer Science Conference
Year:
1989

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Abstract

Theories in terms of causal mechanisms and causal relationships are a critical component of problem solving in artificial intelligence. A theory for explaining a given observation should satisfy constraints based on causal knowledge. In this paper, we present a new approach to theory formation. Under this approach, a theory is formed by reasoning with causal constraints. The reasoning method is constraint-satisfaction. Each coherent set of causal mechanisms discovered by the method instantiates the domain causal model to generate a causal hypothesis. If the domain causal model is true, then it can be shown that one of the causal hypotheses generated is true. In the case of using multi-level constraints, a theory is refined into more details by reasoning top-down through the levels of constraints.