Using expectation-maximization for reinforcement learning
Neural Computation
The Factored Frontier Algorithm for Approximate Inference in DBNs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
ICML '06 Proceedings of the 23rd international conference on Machine learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Action-space partitioning for planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Online learning and exploiting relational models in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Relevance Grounding for Planning in Relational Domains
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Exploration in relational worlds
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Planning with noisy probabilistic relational rules
Journal of Artificial Intelligence Research
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Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models remains a major issue. We propose to convert learned noisy probabilistic relational rules into a structured dynamic Bayesian network representation. Predicting the effects of action sequences using approximate inference allows for planning in complex worlds. We evaluate the effectiveness of our approach for online planning in a 3D simulated blocksworld with an articulated manipulator and realistic physics. Empirical results show that our method can solve problems where existing methods fail.