Explanation-based learning: a survey of programs and perspectives
ACM Computing Surveys (CSUR)
Watch what I do: programming by demonstration
Watch what I do: programming by demonstration
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A General Explanation-Based Learning Mechanism and Its Application to Narrative Understanding
A General Explanation-Based Learning Mechanism and Its Application to Narrative Understanding
Learning programs from traces using version space algebra
Proceedings of the 2nd international conference on Knowledge capture
Learning Recursive Control Programs from Problem Solving
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
A goal- and dependency-directed algorithm for learning hierarchical task networks
Proceedings of the fifth international conference on Knowledge capture
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AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Achieving far transfer in an integrated cognitive architecture
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Learning hierarchical task networks for nondeterministic planning domains
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning HTN method preconditions and action models from partial observations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
The Knowledge Engineering Review
Inductive generalization of analytically learned goal hierarchies
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
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Cognitive Systems Research
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Cognitive Systems Research
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
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Knowledge-based planning methods offer benefits over classical techniques, but they are time consuming and costly to construct. There has been research on learning plan knowledge from search, but this can take substantial computer time and may even fail to find solutions on complex tasks. Here we describe another approach that observes sequences of operators taken from expert solutions to problems and learns hierarchical task networks from them. The method has similarities to previous algorithms for explanation-based learning, but differs in its ability to acquire hierarchical structures and in the generality of learned conditions. These increase the method's capability to transfer learned knowledge to other problems and supports the acquisition of recursive procedures. After presenting the learning algorithm, we report experiments that compare its abilities to other techniques on two planning domains. In closing, we review related work and directions for future research.