Pragmatic navigation: reactivity, heuristics, and search

摘要

FORR (FOr the Right Reasons) is an architecture for learning and problem solving that integrates a possibly incomplete and overlapping set of solution methods to address complex problems. Each method, although it represents some facet of domain expertise, may vary in reliability and speed. The principal contribution of this paper is the extension of FORR to include situation-based behavior (the serial testing of known, triggered techniques for problem solving in a domain) with reactivity and heuristic reasoning. FORR categorizes methods as reactive, heuristic, or situationbased, and addresses problem solving with one category of methods at a time. A hierarchical reasoner first has the opportunity to react correctly. If no ready reaction is computed, the reasoner activates a set of reactive triggers for time-limited search procedures tailored to specific situations. If they, too, fail to produce a response, the reasoner resorts to collaboration among heuristic rationales. All three components reference knowledge learned from experience. In a series of experiments, this architecture is shown to be effective and efficient. Ablation experiments demonstrate how each component plays an important role in problem solving. Additional contributions of this paper include a FORR-based, pragmatic, cognitively plausible approach to navigation with learned heuristic approximations that describe two-dimensional territory and travel experience through it, and a careful study of how situation-based behavior, reactivity, and heuristics interact there. Empirical evidence demonstrates that the resultant system is both effective and efficient, and guidelines for generalization to other domains are provided.