Adaptive Computer Aiding in Dynamic Decision Processes
This report describes in brief a research program directed toward the application of adaptive computer techniques for aiding the human decision maker in dynamic decision processes. Aiding information of several types comes from the on-line acquisition of the decision maker’s value structure by a trainable computer system. A maximum-likelihood model of real-world behavior is used to predict environment-state transitions, and an expected utility model of decision-maker behavior is used to predict, evaluate, and modify or automate operator decisions. The overall system models information-acquisition strategy, as well as action choices. It is presently being implemented on an interactive minicomputer, and applied to a simulated intelligence operation involving surveillance of a mobile fishing fleet using sensors of varying costs and reliabilities. Research goals include experimental investigation of the factors which influence optimal decision aiding in complex, realistic and open intelligence-gathering and decision-making tasks. A major concern is to identify aiding techniques which best match the judgmental abilities of man with the discriminative capacity of an adaptive machine.
KeywordsDecision Environment Linear Discriminant Function Adaptive Computer Fishing Fleet Expected Utility Model
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