Pattern-based learning and spatially oriented concept formation in a multi-agent, decision-making expert

As they gain expertise in problem solving, people increasingly rely on patterns and spatially oriented reasoning. This paper describes an associative visual-pattern classifier and the automated acquisition of new, spatially oriented reasoning agents that simulate such behavior. They are incorporated into a multi-agent game-learning program whose architecture robustly combines agents with conflicting perspectives. When tested on three games, the visual-pattern classifier learns meaningful patterns, and the pattern-based, spatially oriented agents generalized from these patterns are generally correct. The accuracy of the contribution of each of the newly created agents to the decision-making process is measured against an expert opponent, and a perceptron-like algorithm is used to learn game-specific weights for these agents. Much of the knowledge encapsulated by the new agents was previously inexpressible in the program's representation and in some cases is not readily deducible from the rules.