Artificial Intelligence, 32, 247–257.

We present the thesis that planning can be viewed as problem-solving search using subgoals, macro-operators, and abstraction as knowledge sources. Our goal is to quantify problem-solving performance using these sources of knowledge. New results include the identification of subgoal distance as a fundamental measure of problem difficulty, a multiplicative time-space tradeoff for macro-operators, and an analysis of abstraction which concludes that abstraction hierarchies can reduce exponential problems to linear complexity. Artificial Intelligence, 33 (1), 65-88.

"The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities." Artificial Intelligence, 33(1):1-64.

Proc. Tenth Intl. Joint Conference on Artificial Intelligence (pp. 334-337). Milan, Italy: IJCAII.

AI Magazine, 10, 49-56.

AI Magazine, 3

William Heinemann Ltd., London

In IJCAI-87.

In IJCAI-87, Proc. Tenth Intl. Joint Conference on Artificial Intelligence, Milan, Italy: Morgan Kaufmann, 1987, Vol. 1, pp. 264-270.

This paper describes a visual feedback control system which is able to guide road vehicle on well structured roads at high speeds. The road boundary markings are tracked by a multiprocessor image processing system using contour correlation and curvature models together with the laws of perspective projection.