Title :
Symbolic argumentation for decision making under uncertainty
Author_Institution :
Decision Manage. Group, Charles River Anal. Inc., Cambridge, MA, USA
Abstract :
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.
Keywords :
decision making; inference mechanisms; knowledge representation languages; probability; uncertainty handling; Dempster-Shafer theory; decision making; high-level knowledge representation language; subjective probability; symbolic argumentation; uncertainty method; Aggregates; Decision making; Humans; Knowledge management; Knowledge representation; Military computing; Missiles; Rivers; Robustness; Uncertainty; Dempster-Shafer theory; argumentation; belief function; decision-making;
Conference_Titel :
Information Fusion, 2005 8th International Conference on
Print_ISBN :
0-7803-9286-8
DOI :
10.1109/ICIF.2005.1591967