Representing and Solving Decision Problems with Limited Information
- 1 September 2001
- journal article
- Published by Institute for Operations Research and the Management Sciences (INFORMS) in Management Science
- Vol. 47 (9) , 1235-1251
- https://doi.org/10.1287/mnsc.47.9.1235.9779
Abstract
We introduce the notion of LImited Memory Influence Diagram (LIMID) to describe multistage decision problems in which the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constraints, such as in partially observed Markov decision processes (POMDPs). We give an algorithm for improving any given strategy by local computation of single policy updates and investigate conditions for the resulting strategy to be optimal.Local Computation, Message Passing, Optimal Strategies, Partially Observed Markov Decision Process, Single Policy UpdatingKeywords
This publication has 11 references indexed in Scilit:
- A note about redundancy in influence diagramsInternational Journal of Approximate Reasoning, 1998
- From Influence Diagrams to Junction TreesPublished by Elsevier ,1994
- Valuation-Based Systems for Bayesian Decision AnalysisOperations Research, 1992
- Optimal decomposition of probabilistic networks by simulated annealingStatistics and Computing, 1992
- A survey of algorithmic methods for partially observed Markov decision processesAnnals of Operations Research, 1991
- A survey of solution techniques for the partially observed Markov decision processAnnals of Operations Research, 1991
- Axioms for Probability and Belief-Function PropagationPublished by Elsevier ,1990
- Causal Networks: Semantics and Expressiveness* *This work was partially supported by the National Science Foundation Grant #IRI-8610155. “Graphoids: A Computer Representation for Dependencies and Relevance in Automated Reasoning (Computer Information Science).”Published by Elsevier ,1990
- Evaluating Influence DiagramsOperations Research, 1986
- A Constraint – Propagation Approach to Probabilistic Reasoning* *This work was supported in part by the National Science Foundation, Grant #DSR 83–13875Published by Elsevier ,1986