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 present a generic method, based on heuristic search. 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 benchmarks from the probabilistic planning competition.