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.

Reported properties of human decision-making under time pressure are used to refme a hybrid, hierarchical re;isoner. The resultant system is used to explore the relationships among re?activity, heuristic reasoning, situation-based behavior, seiirch, and learning. The program first has the opportunity to react correctly. If no ready reaction is computed, the reasoner acti?vates a set of time-limited search procedures. If any one of them succeeds, it produces a sequence of actions to be exe?cuted. If they fail to produce a response, the reasoner resorts to collaboration among a set of heuristic rationales. A time?limited maze-exploration task is posed where traditional AI techniques fail, but this hybrid reasoner succeeds. In a series of experiments, the hybrid is shown to be both effective and efficient. The data also show how correct reaction, time-lim?ited search with reactive trigger, heuristic reasoning, and learning each play an important role in problem solving. Re?activity is demonstrably enhanced by brief, situation-based, intelligent searches to generate solution fragments.

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.

The subject of this paper is the role of transferable commonsense principles in the acquisition of gameplaying expertise. W e argue that individuals skilled in a domain develop expertise because they know and apply these principles, and that most game-playing programs do not play like people. The paper describes Hoyle, a model of an expert game player that relies on the use of commonsense principles, limited memory, and useful knowledge to learn to play two-person, perfect information finite-board games expertly. W e then describe an experiment in which human subjects played three such games against a computer expert. After playing these games, the subjects evaluated Hoyle's game-playing principles in the context of their own behavior Verbal protocols and subjects' evaluations revealed considerable overlap between the principles preferred by our subjects and those preferred by Hoyle. Using learning time as a measure of difficulty, the subjects' performance and Hoyle's performance ordered the three games identically. This experiment also revealed differences in the use of gameplaying principles between skilled and unskilled players: skilled players judged the game-playing principles to be more effective than did unskilled players, skilled players used several different principles while unskilled players relied on one principle, and skilled players anticipated their opponent's moves while unskilled players merely reacted.

This paper describes how a cognitive architecture builds a spatial model and navigates from it without a map. Each constructed model is a collage of spatial affordances that describes how the environment has been sensed and traversed. The system exploits the evolving model while it directs an agent to explore the environment. Effective models are learned quickly during travel. Moreover, when combined with simple heuristics, the learned spatial model supports effective navigation. In three simple environments, these learned models describe space in ways familiar to people, and often produce near-optimal travel times