Planning by Probabilistic Inference

Hagai Attias
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, PMLR R4:9-16, 2003.

Abstract

This paper presents and demonstrates a new approach to the problem of planning under uncertainty. Actions are treated as hidden variables, with their own prior distributions, in a probabilistic generative model involving actions and states. Planning is done by computing the posterior distribution over actions, conditioned on reaching the goal state within a specified number of steps. Under the new formulation, the toolbox of inference techniques be brought to bear on the planning problem. This paper focuses on problems with discrete actions and states, and discusses some extensions.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR4-attias03a, title = {Planning by Probabilistic Inference}, author = {Attias, Hagai}, booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics}, pages = {9--16}, year = {2003}, editor = {Bishop, Christopher M. and Frey, Brendan J.}, volume = {R4}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r4/attias03a/attias03a.pdf}, url = {https://proceedings.mlr.press/r4/attias03a.html}, abstract = {This paper presents and demonstrates a new approach to the problem of planning under uncertainty. Actions are treated as hidden variables, with their own prior distributions, in a probabilistic generative model involving actions and states. Planning is done by computing the posterior distribution over actions, conditioned on reaching the goal state within a specified number of steps. Under the new formulation, the toolbox of inference techniques be brought to bear on the planning problem. This paper focuses on problems with discrete actions and states, and discusses some extensions.}, note = {Reissued by PMLR on 01 April 2021.} }
Endnote
%0 Conference Paper %T Planning by Probabilistic Inference %A Hagai Attias %B Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2003 %E Christopher M. Bishop %E Brendan J. Frey %F pmlr-vR4-attias03a %I PMLR %P 9--16 %U https://proceedings.mlr.press/r4/attias03a.html %V R4 %X This paper presents and demonstrates a new approach to the problem of planning under uncertainty. Actions are treated as hidden variables, with their own prior distributions, in a probabilistic generative model involving actions and states. Planning is done by computing the posterior distribution over actions, conditioned on reaching the goal state within a specified number of steps. Under the new formulation, the toolbox of inference techniques be brought to bear on the planning problem. This paper focuses on problems with discrete actions and states, and discusses some extensions. %Z Reissued by PMLR on 01 April 2021.
APA
Attias, H.. (2003). Planning by Probabilistic Inference. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R4:9-16 Available from https://proceedings.mlr.press/r4/attias03a.html. Reissued by PMLR on 01 April 2021.

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