How can computer vision systems be designed to handle unfamiliar or unexpected scenes? Many of the current systems cope quite well with limited visual worlds, by making use of specialized knowledge about these worlds- But if we want to expand these systems to handle a wide range of visual domains, it will not be enough to simply employ a large number of specialized models. It will also be important to make use of what might be called "general-purpose models. " Such models can be used to suggest reasonable descriptions for a given scene, e.g., in terms of

Recent research demonstrates the use of goal regression as an analytic technique for learning search heuristics. This paper critically examines this research and identifies essential applicability conditions for the technique. The conditions that operators be invertible and that the domain be closed with respect to the inverse operators severely limit the use of analytic goal regression. In those restricted domains which satisfy the applicability conditions, analytic goal regression only discovers required preconditions for operator application. Discovering pragmatic preconditions is beyond the capability of the technique. An alternative, called experimental goal regression, is defined which approximates the results of analytic goal regression without suffering from these limitations. I.

this paper, we try to define the concept of virtual actors and show their evolution from clones and guided actors to autonomous, perceptive, and emotional actors. Examples of behaviors with limited intelligence are presented: knowledge-based navigation, locomotion, grasping and game playing