An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient structure learning in factored-state MDPs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Efficient reinforcement learning in factored MDPs
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Exploring compact reinforcement-learning representations with linear regression
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Incremental Learning of Relational Action Rules
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Knows what it knows: a framework for self-aware learning
Machine Learning
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We study the problem of learning stochastic actions in propositional, factored environments, and precisely the problem of identifying STRIPS-like effects from transitions in which they are ambiguous. We give an unbiased, maximum likelihood approach, and show that maximally likely actions can be computed efficiently from observations. We also discuss how this study can be used to extend an RL approach for actions with independent effects to one for actions with correlated effects.