Proceedings of the Annual Meeting of the Cognitive Science Society

As they gain expertise in problem solving, people increasingly rely on patterns and spatially-oriented reasoning. This paper describes the integration of an associative visual pattern classifier and the automated acquisition of new, spatially-oriented reasoning agents that simulate such behavior. They are incorporated into a game-learning program whose architecture robustly combines agents with conflicting perspectives. When tested on three games, the visual pattern classifier leams meaningful patterns, and the pattern-based, spatially-oriented agents generalized fi-om these patterns are generally correct. The trustworthiness and relevance of these agents are confirmed with an algorithm that measures the accuracy of the contribution of each agent to the decision-making process. Much of the knowledge encapsulated by the correct new agents was previously inexpressible in the program's representation and in some cases is not readily deducible from the rules.