Proceedings of the Annual Meeting of the Cognitive Science Society

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.