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- W2157457978 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." @default.
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- W2157457978 date "2011-01-01" @default.
- W2157457978 modified "2023-09-23" @default.
- W2157457978 title "Influence Diagrams with Memory States: Representation and Algorithms" @default.
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- W2157457978 doi "https://doi.org/10.1007/978-3-642-24873-3_23" @default.
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