A POMDP Approach to Influence Diagram Evaluation
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3124-3131.
Abstract
We propose a node-removal/arc-reversal algorithm for influence diagram evaluation that includes reductions that allow an influence diagram to be solved by a generalization of the dynamic programming approach to solving partially observable Markov decision processes (POMDPs). Among its potential advantages, the algorithm allows a more flexible ordering of node removals, and a POMDP-inspired approach to optimizing over hidden state variables, which can improve the scalability of influence diagram evaluation in solving complex, multi-stage problems. It also finds a more compact representation of an optimal strategy.
@inproceedings{ijcai2016p443,
title = {A POMDP Approach to Influence Diagram Evaluation},
author = {Hansen, Eric A. and Shi, Jinchuan and Khaled, Arindam},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3124--3131},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/443},
}
TY - CPAPER TI - A POMDP Approach to Influence Diagram Evaluation AU - Hansen, Eric A. AU - Shi, Jinchuan AU - Khaled, Arindam T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3124 EP - 3131 UR - https://ijcai.org/proceedings/2016/443 ER -Copied
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