Decision Making: A Computational Approach

The objective of our research is to incorporate into a computerized environment the intrinsic capabilities of an intelligent decision-maker to function and decide in an environment characterized by a lack of information, inconsistency and ambiguity. There is much (though not all) that decision-makers do in realistic situations that is sound and reasonable, in view of all the constraints to which they are subject. Our perspective draws upon research in cognitive psychology as to how people construct and revise beliefs. We show how such a perspective leads us to a model of an artificially intelligent (A.I.) decision-making system that can reason with incomplete knowledge, make revisions in beliefs due to new evidence, and modify the reasoning model itself when it is so warranted. Such a capability is achieved by a synergistic combination of several metaphors of reasoning and learning. In particular, the model integrates three previously unrelated themes developed in the A.I. field: explanation based learning, truth maintenance systems and analogical reasoning.

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