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Empirical speedup learning of decomposition rules for planning
One current research goal of Artificial Intelligence and Machine Learning is to build learning systems that robustly improve their planning performance with experience [Tade91]. This work concentrates on learning decomposition rules, i.e., learning rules that guide the planning process by determining the order in which operators are to be applied and how they are to be bound in specific states. A domain-independent learning algorithm that is capable of learning such rules from teacher-given examples has been designed and implemented. Decomposition rules have been learned in the blocks world domain , and it is shown that a small number of examples are sufficient to achieve very high success rates.Keywords: Empirical learning , speedup learning, decomposition rules, maximally specific generalizations , subset queries