2010/03/27 by David Tolpin, Tolpin, David, Solomon Eyal Shimony +1
Computer Science · #AI-based Problem Solving and Planning #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1003.5305
7 pages, 2 figures, presented at URPDM2010; plots fixed
openalex publication_date 2010/03/27 · arxiv created 2010/04/16 · arxiv updated 2015/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Computing value of information (VOI) is a crucial task in various aspects of decision-making under uncertainty, such as in meta-reasoning for search; in selecting measurements to make, prior to choosing a course of action; and in managing the exploration vs. exploitation tradeoff. Since such applications typically require numerous VOI computations during a single run, it is essential that VOI be computed efficiently. We examine the issue of anytime estimation of VOI, as frequently it suffices to get a crude estimate of the VOI, thus saving considerable computational resources. As a case study, we examine VOI estimation in the measurement selection problem. Empirical evaluation of the proposed scheme in this domain shows that computational resources can indeed be significantly reduced, at little cost in expected rewards achieved in the overall decision problem.