Redesigning Stochastic Environments for Maximized Utility

​We present the Utility Maximizing Design (UMD) model​ for optimally redesigning stochastic environments to achieve maximized performance. This model suits well contemporary ​​applications that involve the design of environments where robots and humans co-exist an co-operate, e.g., vacuum cleaning robot. We discuss two special cases of the UMD model. The first is the equi-reward UMD (ER-UMD)​ ​in which the agents and the system share a utility function, such as for the vacuum cleaning robot. The second is the goal​ ​recognition design (GRD) setting, discussed in the literature, in which system and agent utilities are independent. To find the set of optimal​​ modifications to apply to a UMD model, we propose the use of heuristic search, extending previous methods used for GRD settings. After specifying the conditions for optimality in the​ general case, we present an admissible heuristic for the ER-UMD case. We also present a novel compilation that embeds​ the redesign process into a planning problem, allowing use of any off-the-shelf solver to find the best way to modify an environment when a design budget is specified. Our evaluation shows the feasibility of the approach using standard bench​​marks from the probabilistic planning competition.​