Explanation-based learning: a problem solving perspective
Artificial Intelligence
Robust trainability of single neurons
Journal of Computer and System Sciences
Learning action strategies for planning domains
Artificial Intelligence
Artificial Intelligence
Machine Learning Methods for Planning
Machine Learning Methods for Planning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Using genetic programming to learn and improve control knowledge
Artificial Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Declarative Control Rules for Constraint-BAsed Planning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
On learning linear ranking functions for beam search
Proceedings of the 24th international conference on Machine learning
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
The FF planning system: fast plan generation through heuristic search
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
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
Learnability of bipartite ranking functions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Scaling up heuristic planning with relational decision trees
Journal of Artificial Intelligence Research
Structured perceptron with inexact search
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Beam search is commonly used to help maintain tractability in large search spaces at the expense of completeness and optimality. Here we study supervised learning of linear ranking functions for controlling beam search. The goal is to learn ranking functions that allow for beam search to perform nearly as well as unconstrained search, and hence gain computational efficiency without seriously sacrificing optimality. In this paper, we develop theoretical aspects of this learning problem and investigate the application of this framework to learning in the context of automated planning. We first study the computational complexity of the learning problem, showing that even for exponentially large search spaces the general consistency problem is in NP. We also identify tractable and intractable subclasses of the learning problem, giving insight into the problem structure. Next, we analyze the convergence of recently proposed and modified online learning algorithms, where we introduce several notions of problem margin that imply convergence for the various algorithms. Finally, we present empirical results in automated planning, where ranking functions are learned to guide beam search in a number of benchmark planning domains. The results show that our approach is often able to outperform an existing state-of-the-art planning heuristic as well as a recent approach to learning such heuristics.