Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
A Survey on Case-Based Planning
Artificial Intelligence Review
Learning Generalized Policies from Planning Examples Using Concept Languages
Applied Intelligence
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
The Knowledge Engineering Review
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
HTN-MAKER: learning HTNs with minimal additional knowledge engineering required
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Adaptive control for autonomous underwater vehicles
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Kernel functions for case-based planning
Artificial Intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Scaling up heuristic planning with relational decision trees
Journal of Artificial Intelligence Research
Dynamic planning approach to automated web service composition
Applied Intelligence
A universal planning system for hybrid domains
Applied Intelligence
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Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.