Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2011
Abstract
Influence diagrams (IDs) offer a powerful framework for decision making under uncertainty, but their applicability has been hindered by the exponential growth of runtime and memory usage--largely due to the no-forgetting assumption. We present a novel way to maintain a limited amount of memory to inform each decision and still obtain near-optimal policies. The approach is based on augmenting the graphical model with memory states that represent key aspects of previous observations--a method that has proved useful in POMDP solvers. We also derive an efficient EM-based message-passing algorithm to compute the policy. Experimental results show that this approach produces highquality approximate polices and offers better scalability than existing methods.
Discipline
Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization
Publication
Algorithmic decision theory: 2nd International Conference, ADT 2011, Piscataway, NJ, October 26-28: Proceedings
Volume
6992
First Page
306
Last Page
319
ISBN
9783642248726
Identifier
10.1007/978-3-642-24873-3_23
Publisher
Springer Verlag
City or Country
Berlin
Citation
WU, Xiaojian; KUMAR, Akshat; and ZILBERSTEIN, Shlomo.
Influence Diagrams With Memory States: Representation and Algorithms. (2011). Algorithmic decision theory: 2nd International Conference, ADT 2011, Piscataway, NJ, October 26-28: Proceedings. 6992, 306-319.
Available at: https://ink.library.smu.edu.sg/sis_research/2206
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-642-24873-3_23
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons