2013/02/13 by Mathias Bauer, Bauer, Mathias
Computer Science · Decision Sciences · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Multi-Criteria Decision Making #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.3557
Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)
arxiv created 2013/02/13 · openalex publication_date 2013/02/13 · arxiv updated 2013/02/18 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief functions involved. Besides introducing a new algorithm using this method, this paper describes an empirical study that examines the appropriateness of these approximation procedures in decision making situations. It presents the empirical findings and discusses the various tradeoffs that have to be taken into account when actually applying one of these methods.