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arXiv · 1109.1754

Solving Limited Memory Influence Diagrams

Denis Deratani Mauá·Cassio Polpo de Campos·Marco Zaffalon
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Abstract

We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and $10^{64}$ solutions. We show that the problem is NP-hard even if the underlying graph structure of the problem has small treewidth and the variables take on a bounded number of states, but that a fully polynomial time approximation scheme exists for these cases. Moreover, we show that the bound on the number of states is a necessary condition for any efficient approximation scheme.

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Denis Deratani Mauá, Cassio Polpo de Campos, Marco Zaffalon. 2011-09-08. Solving Limited Memory Influence Diagrams. https://arxiv.org/abs/1109.1754

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