Explanation-based learning: a problem solving perspective
Artificial Intelligence
Taxonomic syntax for first order inference
Journal of the ACM (JACM)
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Machine Learning
Learning action strategies for planning domains
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Machine Learning Methods for Planning
Machine Learning Methods for Planning
Neuro-Dynamic Programming
Using genetic programming to learn and improve control knowledge
Artificial Intelligence
Machine Learning
Learning Declarative Control Rules for Constraint-BAsed Planning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Discovering State Constraints in DISCOPLAN: Some New Results
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A logical measure of progress for planning
Eighteenth national conference on Artificial intelligence
Learning measures of progress for planning domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Discriminative learning of beam-search heuristics for planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Multi-strategy learning of search control for partial-order planning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Inductive policy selection for first-order MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Constraint-Based Case-Based Planning Using Weighted MAX-SAT
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning Linear Ranking Functions for Beam Search with Application to Planning
The Journal of Machine Learning Research
Finding and transferring policies using stored behaviors
Autonomous Robots
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
GA-FreeCell: evolving solvers for the game of FreeCell
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Scaling up heuristic planning with relational decision trees
Journal of Artificial Intelligence Research
Learning heuristic functions for large state spaces
Artificial Intelligence
Macro learning in planning as parameter configuration
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
Goal distance estimation for automated planning using neural networks and support vector machines
Natural Computing: an international journal
HH-evolver: a system for domain-specific, hyper-heuristic evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A case-based approach to heuristic planning
Applied Intelligence
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A number of today's state-of-the-art planners are based on forward state-space search. The impressive performance can be attributed to progress in computing domain independent heuristics that perform well across many domains. However, it is easy to find domains where such heuristics provide poor guidance, leading to planning failure. Motivated by such failures, the focus of this paper is to investigate mechanisms for learning domain-specific knowledge to better control forward search in a given domain. While there has been a large body of work on inductive learning of control knowledge for AI planning, there is a void of work aimed at forward-state-space search. One reason for this may be that it is challenging to specify a knowledge representation for compactly representing important concepts across a wide range of domains. One of the main contributions of this work is to introduce a novel feature space for representing such control knowledge. The key idea is to define features in terms of information computed via relaxed plan extraction, which has been a major source of success for non-learning planners. This gives a new way of leveraging relaxed planning techniques in the context of learning. Using this feature space, we describe three forms of control knowledge---reactive policies (decision list rules and measures of progress) and linear heuristics---and show how to learn them and incorporate them into forward state-space search. Our empirical results show that our approaches are able to surpass state-of-the-art non-learning planners across a wide range of planning competition domains.