2013/02/13 by Vu Ha, Vu A. Ha, Ha, Vu A. +2 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.3581
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 2025/10/10 · openalex updated_date 2026/07/28
Modeling worlds and actions under uncertainty is one of the central problems in the framework of decision-theoretic planning. The representation must be general enough to capture real-world problems but at the same time it must provide a basis upon which theoretical results can be derived. The central notion in the framework we propose here is that of the affine-operator, which serves as a tool for constructing (convex) sets of probability distributions, and which can be considered as a generalization of belief functions and interval mass assignments. Uncertainty in the state of the worlds is modeled with sets of probability distributions, represented by affine-trees while actions are defined as tree-manipulators. A small set of key properties of the affine-operator is presented, forming the basis for most existing operator-based definitions of probabilistic action projection and action abstraction. We derive and prove correct three projection rules, which vividly illustrate the precision-complexity tradeoff in plan projection. Finally, we show how the three types of action abstraction identified by Haddawy and Doan are manifested in the present framework.