Learning to Search. From Weak Methods to Domain-Specific Heuristics.

摘要

Learning from experience involves three distinct components - generating behavior, assigning credit, and modifying behavior. This document discusses these components in the context of learning search heuristics, along with the types of learning that can occur. The author then focus on SAGE, a system that improves its search strategies with practice. The program is implemented as a production system, and learns by creating and strengthening rules for proposing moves. SAGE incorporates five different heuristics for assigning credit and blame, and employs a discrimination process to direct its search through the space of rules. The system has shown its generality by learning heuristics for directing search in six different task domains. In addition to improving its search behavior on practice problems, SAGE is able to transfer its expertise to scaled-up versions of a task, and in one case transfers its acquired search strategy to problems with different initial and goal states.