Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning
Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning
Explanation-Based Generalization: A Unifying View
Machine Learning
Learning Goal-Decomposition Rules using Exercises
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Applications of SHOP and SHOP2
IEEE Intelligent Systems
Learning approximate preconditions for methods in hierarchical plans
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning hierarchical task networks by observation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning Recursive Control Programs from Problem Solving
The Journal of Machine Learning Research
A domain-independent system for case-based task decomposition without domain theories
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Building and refining abstract planning cases by change of representation language
Journal of Artificial Intelligence Research
A goal- and dependency-directed algorithm for learning hierarchical task networks
Proceedings of the fifth international conference on Knowledge capture
Learning hierarchical task networks for nondeterministic planning domains
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning probabilistic hierarchical task networks to capture user preferences
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
Inductive generalization of analytically learned goal hierarchies
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
A new representation and associated algorithms for generalized planning
Artificial Intelligence
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
iFALCON: A neural architecture for hierarchical planning
Neurocomputing
An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
ACM Transactions on Intelligent Systems and Technology (TIST)
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
A case-based approach to heuristic planning
Applied Intelligence
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We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTN-MAKER takes as input the initial states from a set of classical planning problems in a planning domain and solutions to those problems, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic information in order to determine which portions of the input plans accomplish a particular task and constructs HTN methods based on those analyses. Our theoretical results show that HTN-MAKER is sound and complete. We also present a formalism for a class of planning problems that are more expressive than classical planning. These planning problems can be represented as HTN planning problems. We show that the methods learned by HTN-MAKER enable an HTN planner to solve those problems. Our experiments confirm the theoretical results and demonstrate convergence in three well-known planning domains toward a set of HTN methods that can be used to solve nearly any problem expressible as a classical planning problem in that domain, relative to a set of goals.