Optimal selection of uncertain actions by maximizing expected utility

A new means of action selection via utility fusion is introduced as an alte rnative to both sensor fusion and command fusion. Distributed asynchronous behaviors indicate the utility of various possible states and their associa ted uncertainty. A centralized arbiter then combines these utilities and pr obabilities to determine the optimal action based on the maximization of ex pected utility. The construction of a utility map allows the system being c ontrolled to be modeled and compensated for; experimental results verify th at this approach improves performance.