Solving Limited Memory Influence Diagrams

D. D. Mauá, C. P. de Campos, M. Zaffalon

Research output: Working paper


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.
Original languageEnglish
Publication statusPublished - 01 Sept 2011

Bibliographical note

CoRR ArXiv e-prints 1109.1754


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