Artificial Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth 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 evaluation functions to improve optimization by local search
The Journal of Machine Learning Research
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
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
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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
Structured machine learning: the next ten years
Machine Learning
The factored policy-gradient planner
Artificial Intelligence
Learning Linear Ranking Functions for Beam Search with Application to Planning
The Journal of Machine Learning Research
Scaling up heuristic planning with relational decision trees
Journal of Artificial Intelligence Research
An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
ACM Transactions on Intelligent Systems and Technology (TIST)
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We consider the problem of learning heuristics for controlling forward state-space beam search in AI planning domains. We draw on a recent framework for "structured output classification" (e.g. syntactic parsing) known as learning as search optimization (LaSO). The LaSO approach uses discriminative learning to optimize heuristic functions for search-based computation of structured outputs and has shown promising results in a number of domains. However, the search problems that arise in AI planning tend to be qualitatively very different from those considered in structured classification, which raises a number of potential difficulties in directly applying LaSO to planning. In this paper, we discuss these issues and describe a LaSO-based approach for discriminative learning of beam-search heuristics in AI planning domains. We give convergence results for this approach and present experiments in several benchmark domains. The results show that the discriminatively trained heuristic can outperform the one used by the planner FF and another recent non-discriminative learning approach.