Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Automatically generating abstractions for planning
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Learning hierarchical task networks by observation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Encoding of Planning Problems and Their Optimizations in Linear Logic
Applications of Declarative Programming and Knowledge Management
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Filtering, decomposition and search space reduction for optimal sequential planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
TALplanner in the third international planning competition: extensions and control rules
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
The detection and exploitation of symmetry in planning problems
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Strategies for learning search control rules: an explanation-based approach
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Combining weak learning heuristics in general problem solvers
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Selectively generalizing plans for problem-solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Learning by discovering macros in puzzle solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
A parametric hierarchical planner for experimenting abstraction techniques
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Embracing causality in specifying the indirect effects of actions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Branching and pruning: An optimal temporal POCL planner based on constraint programming
Artificial Intelligence
Causal theories of action and change
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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There are many approaches for solving planning problems. Many of these approaches are based on ‘brute force’ search methods and they usually do not care about structures of plans previously computed in particular planning domains. By analyzing these structures, we can obtain useful knowledge that can help us find solutions to more complex planning problems. The method described in this paper is designed for gathering macro-operators by analyzing training plans. This sort of analysis is based on the investigation of action dependencies in training plans. Knowledge gained by our method can be passed directly to planning algorithms to improve their efficiency.