Proceedings of the Annual Meeting of the Cognitive Science Society

The extent to which concepts, memory, and planning are necessary to the simulation of intelligent behavior is a fundamental philosophical issue in AI. A n active and productive segment of the research community has taken the position that multiple low-level agents, properly organized, can account for high-level behavior. The empirical research relevant to this debate with fully operational systems has thus far been primarily on mobile robots that do simple tasks. This paper recounts experiments with Hoyle, a system in a cerebral, rather than a physical, domain. The program learns to perform well and quickly, often outpacing its human creators at two-person, perfect information board games. Hoyle demonstrates that a surprising amount of intelligent behavior can be treated as if it were situation-determined, that often planning is unnecessary, and that the memory required to support this learning is minimal. The contribution of this paper is its demonstration of h ow explicit, rather than implicit, concept representation strengthens a reactive system that learns, and reduces its reliance on memory.