Equi-Reward Utility Maximizing Design in Stochastic Environments
Abstract
We present the Equi Reward Utility Maximizing Design (ER-UMD) problem for redesigning stochastic environments to maximize agent performance. ER-UMD fits well contemporary applications that require offline design of environments where robots and humans act and cooperate. To find an optimal modification sequence we present two novel solution techniques: a compilation that embeds design into a planning problem, allowing use of off-the-shelf solvers to find a solution, and a heuristic search in the modifications space, for which we present an admissible heuristic. Evaluation shows the feasibility of the approach using standard benchmarks from the probabilistic planning competition and a benchmark we created for a vacuum cleaning robot setting.
Cite
Text
Keren et al. "Equi-Reward Utility Maximizing Design in Stochastic Environments." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/608Markdown
[Keren et al. "Equi-Reward Utility Maximizing Design in Stochastic Environments." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/keren2017ijcai-equi/) doi:10.24963/IJCAI.2017/608BibTeX
@inproceedings{keren2017ijcai-equi,
title = {{Equi-Reward Utility Maximizing Design in Stochastic Environments}},
author = {Keren, Sarah and Pineda, Luis Enrique and Gal, Avigdor and Karpas, Erez and Zilberstein, Shlomo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {4353-4360},
doi = {10.24963/IJCAI.2017/608},
url = {https://mlanthology.org/ijcai/2017/keren2017ijcai-equi/}
}