Optimal Selection of Uncertain Actions by Maximizing Expected Utility

A new means of action selection via utility fusion is introduced as an alternative to both sensor fusion and command fusion. Distributed asynchronous behaviors indicate the utility of various possible states and their associated uncertainty. A centralized arbiter then combines these utilities and probabilities to determine the optimal action based on the maximization of expected utility. The construction of a utility map allows the system being controlled to be modeled and compensated for; experimental results verify that this approach improves performance.