On the Roles of Search and Learning in Time-Limited Decision Making
Susan L. Epstein · 1995
Reported properties of human decision-making under time pressure are used to refine a hybrid, hierarchical reasoner. The resultant system is used to explore the relationships among reactivity, heuristic reasoning, situation-based behavior, search, and learning. The program first has the opportunity to react correctly. If no ready reaction is computed, the reasoner activates a set of time-limited search procedures. If any one of them succeeds, it produces a sequence of actions to be executed. If they fail to produce a response, the reasoner resorts to collaboration among a set of heuristic rationales. A timelimited 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-limited search with reactive trigger, heuristic reasoning, and learning each play an important role in problem solving. Reactivity is demonstrably enhanced by brief, situation-based, intelligent searches to generate solution fragments. 1.