Chris Brown's current activities center around building behaving, visually guided systems. The vision work spans a range of topics from traditional symbolic artificial intelligence to low-level aspects of control and includes assembling hardware and software support systems. Learning is a basic capability that relieves the AI programmer from the impossible burden of anticipating all possible situations. Learning is a very active topic at Rochester. Some work on learning control structures, skilled action sequences, and three-dimensional object representations is described in the November 1991 issue of the International Journal of Computer Vision, which is devoted to vision research at Rochester.
More recent work involves learning control strategies for skilled sensori-motor tasks.
Report looks at the problem of visual development to assess the adequacy or necessity of current learning models to explain the acquisition of visual abilities. We are using Bayesian probability theory and decision analysis to develop a theory of selective visual attention and task-oriented vision, and using it to direct our binocular robot head. Integration of sensorimotor systems in a parallel computational environment is one of our ongoing goals. Recent Collaborators Recent and Ancient Publications Coombs, D. J. and C. M. Brown. Marsh, B. et al.