A Decision Calculus for Belief Functions in Valuation-Based Systems
This work addresses decision-making under uncertainty for researchers in AI and uncertainty reasoning, but it appears incremental as it extends existing frameworks rather than introducing a new paradigm.
The paper tackles the problem of decision-making under uncertainty in Dempster-Shafer theory by proposing a decision calculus within the valuation-based system framework, enabling the solution of decision problems using a fusion algorithm and showing reduction to Bayesian probability theory.
Valuation-based system (VBS) provides a general framework for representing knowledge and drawing inferences under uncertainty. Recent studies have shown that the semantics of VBS can represent and solve Bayesian decision problems (Shenoy, 1991a). The purpose of this paper is to propose a decision calculus for Dempster-Shafer (D-S) theory in the framework of VBS. The proposed calculus uses a weighting factor whose role is similar to the probabilistic interpretation of an assumption that disambiguates decision problems represented with belief functions (Strat 1990). It will be shown that with the presented calculus, if the decision problems are represented in the valuation network properly, we can solve the problems by using fusion algorithm (Shenoy 1991a). It will also be shown the presented decision calculus can be reduced to the calculus for Bayesian probability theory when probabilities, instead of belief functions, are given.