Anytime Decision Making with Imprecise Probabilities
This work addresses decision-making challenges in AI and operations research by enabling flexible, time-bounded solutions, though it appears incremental as it builds on prior anytime and probabilistic frameworks.
The paper tackles the problem of decision making under constraints of uncertain representation and limited computation time, presenting anytime algorithms that adapt existing probabilistic reasoning systems to provide solutions within time limits.
This paper examines methods of decision making that are able to accommodate limitations on both the form in which uncertainty pertaining to a decision problem can be realistically represented and the amount of computing time available before a decision must be made. The methods are anytime algorithms in the sense of Boddy and Dean 1991. Techniques are presented for use with Frisch and Haddawy's [1992] anytime deduction system, with an anytime adaptation of Nilsson's [1986] probabilistic logic, and with a probabilistic database model.