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)

The paper proposes a formal account of Aristotle's trichotomy of verbs, in terms of properties of their continuous tensings, into S(state)-verbs, K(kinesis)-verbs, and E-(energeia)-verbs. Within a Fregean tense framework in which predicates are relativized to times, an account of the continuous tenses is presented and a preliminary account of the trichotomy devised, which permits an illuminating analogy to be drawn between the temporal properties of E- and K-verbs and the spatial properties of stuffs and substances. This analogy is drawn upon (...) in constructing a sophisticated version of the preliminary theory accommodating more of the linguistic data. (shrink)

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first‐order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It (...) may be possible to implement a temporal‐inference machine based on this logic, which keeps track of several “maps” of a time line, one per possible history. (shrink)