Symbolic argumentation for decision making under uncertainty

We present a generic argumentation-based framework for making decisions under uncertainty by fusing knowledge from multiple sources. In this framework, arguments for decision options are expressed in a high-level knowledge representation language incorporating subjective probabilities from decision makers representing the argument strengths. To aggregate a set of such probabilistic arguments for and against the decision options, we apply Dempster-Shafer theory to compute degrees of belief for decision options. Evidence converted from the underlying knowledge base is used to compute degrees of belief, and hence rankings, among the decision options. Decision-making based on such degrees of belief is therefore based on a strong mathematical foundation. The proposed decision making framework has been successfully applied in a variety of domains ranging from theater missile defense and army fire support to medical diagnosis.