2013/02/13 by Fábio Gagliardi Cozman, Fabio Gagliardi Cozman, Cozman, Fabio Gagliardi +2 · 2 citations
Computer Science · Decision Sciences · Engineering · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Auction Theory and Applications #FOS: Computer and information sciences #Machine Learning and Algorithms #Robotics and Sensor-Based Localization #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.3570
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 2016/11/04 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
Quasi-Bayesian theory uses convex sets of probability distributions and expected loss to represent preferences about plans. The theory focuses on decision robustness, i.e., the extent to which plans are affected by deviations in subjective assessments of probability. The present work presents solutions for plan generation when robustness of probability assessments must be included: plans contain information about the robustness of certain actions. The surprising result is that some problems can be solved faster in the Quasi-Bayesian framework than within usual Bayesian theory. We investigate this on the planning to observe problem, i.e., an agent must decide whether to take new observations or not. The fundamental question is: How, and how much, to search for a "best" plan, based on the robustness of probability assessments? Plan generation algorithms are derived in the context of material classification with an acoustic robotic probe. A package that constructs Quasi-Bayesian plans is available through anonymous ftp.