Lazy Incremental Learning of Control Knowledge for EfficientlyObtaining Quality Plans
Artificial Intelligence Review - Special issue on lazy learning
Engineering and compiling planning domain models to promote validity and efficiency
Artificial Intelligence
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
Combining weak learning heuristics in general problem solvers
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Transferring learned control-knowledge between planners
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Inductive Logic Programming (ilp) methods have proven to succesfully acquire knowledge with very different learning paradigms, such as supervised and unsupervised learning or relational reinforcement learning. However, very little has been done on applying it to General Problem Solving (gps). One of the ilp-based approaches applied to gpsis hamlet. This method learns control rules (heuristics) for a non linear planner, prodigy4.0, which is integrated into the ipsssystem; control rules are used as an effective guide when building the planning search tree. Other learning approaches applied to planning generate macro-operators, building high-level blocks of actions, but increasing the branching factor of the search tree. In this paper, we focus on integrating the two different learning approaches (hamletand macro-operators learning), to improve a planning process. The goal is to learn control rules that decide when to use the macro-operators. This process is successfully applied in several classical planning domains.