First paper introducing probabilisitic decision theory methods to AI problem solving.

Animate vision systems have gaze control mechanisms that can actively position the camera coordinate system in response to physical stimuli. Compared to passive systems, animate systems show that visual computation can be vastly less expensive when considered in the larger context of behavior. The most important visual behavior is the ability to control the direction of gaze. This allows the use of very low resolution imaging that has a high virtual resolution. Using such a system in a controlled way provides (...) additional constraints that dramatically simplify the computations of early vision. Another important behavior is the way the environment “behaves‘. Animate systems under real-time constraints can further reduce their computational burden by using environmental cues that are perspicuous in the local context. A third source of economy is introduced when behaviors are learned. Because errors are rarely fatal, systems using learning algorithms can amortize computational cost over extended periods. Further economies can be achieved when the learning system uses indexical reference, which is a form of dynamic variable binding. Animate vision is a natural way of implementing this dynamic binding. (shrink)