Planning by Probabilistic Inference
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
Text
Attias. "Planning by Probabilistic Inference." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.Markdown
[Attias. "Planning by Probabilistic Inference." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.](https://mlanthology.org/aistats/2003/attias2003aistats-planning/)BibTeX
@inproceedings{attias2003aistats-planning,
title = {{Planning by Probabilistic Inference}},
author = {Attias, Hagai},
booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics},
year = {2003},
pages = {9-16},
volume = {R4},
url = {https://mlanthology.org/aistats/2003/attias2003aistats-planning/}
}