Publication Type

Conference Proceeding Article

Publication Date

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

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

International Conference on Algorithmic Decision Theory (ADT)

First Page

306

Last Page

319

ISBN

9783642248726

Identifier

10.1007/978-3-642-24873-3_23

Publisher

Springer Verlag

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