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Until recently, artificial intelligence (AI) research on problem solving ignored issues of uncertainty. With a growing desire to apply research results in real-world contexts, such issues have begun to receive attention. Real-world contexts are inherently uncertain due to several factors, including incomplete and imprecise interpretation of environmental information, unreliable execution of plan actions, and unforeseen interactions among multiple agents. A theoretical framework is offered for addressing issues of problem solving under conditions of uncertainty. The model is a probabilistic generalization of the usual notion of problem space. An admissible forward-directed search algorithm is presented. The need for information-gathering operators to control state disunity and provide pragmatic focusing is established; a representation for such operators is proposed. Aspects of the model are compared to Markov processes and utility-based techniques of decision analysis. A discussion of the limitations of the model is given as well as suggestions for its application.
Systems, Man and Cybernetics, IEEE Transactions on (Volume:SMC-13 , Issue: 4 )
Date of Publication: July-Aug. 1983