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Lecture Notes in Computer Science
The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of adoi:10.1007/978-3-642-23954-0_19 fatcat:vfbz5f7h3venbfwf4f4qjdm2l4