Proceedings:
Learning
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 10
Track:
Learning: Theory
Downloads:
Abstract:
Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem solving performance by solving a user-given set of problems. We also use this model to motivate the notion of "batch problem solving," and argue that it is more congenial to learning than sequential problem solving. Our theoretical results are applicable to all serially decomposable domains. We empirically validate our results in the domain of Eight Puzzle.
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 10