Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
International Conference on Algorithmic Decision Theory (ADT)
WU, X.; KUMAR, Akshat; and Zilberstein, S..
Influence Diagrams With Memory States: Representation and Algorithms. (2011). International Conference on Algorithmic Decision Theory (ADT). 306-319. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2206