Equi-Reward Utility Maximizing Design in Stochastic Environments

Sarah Keren, Luis Enrique Pineda, Avigdor Gal, Erez Karpas, Shlomo Zilberstein

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.