### Abstract

Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.

Original language | English |
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Pages (from-to) | 30-38 |

Number of pages | 9 |

Journal | Artificial Intelligence |

Volume | 205 |

DOIs | |

Publication status | Published - 14 Nov 2013 |

Externally published | Yes |

### Keywords

- Computational complexity
- Decision networks
- Decision theory
- Influence diagrams
- Probabilistic planning

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## Cite this

*Artificial Intelligence*,

*205*, 30-38. https://doi.org/10.1016/j.artint.2013.10.002