Anytime Decision Making with Imprecise Probabilities

TL;DR

This paper explores anytime algorithms for decision making under uncertainty, capable of handling imprecise probabilities and limited computational resources across various systems.

Contribution

It introduces methods for anytime decision making using imprecise probabilities, adapting existing systems for efficient, resource-aware decision processes.

Findings

Effective anytime algorithms for imprecise probabilistic decision making

Integration of decision methods with existing deduction and logic systems

Demonstrated applicability to probabilistic databases

Abstract

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.