Probabilistic inference and influence diagrams
Operations Research
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Factored Frontier Algorithm for Approximate Inference in DBNs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploiting first-order regression in inductive policy selection
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
ICML '06 Proceedings of the 23rd international conference on Machine learning
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Non-parametric policy gradients: a unified treatment of propositional and relational domains
Proceedings of the 25th international conference on Machine learning
The factored policy-gradient planner
Artificial Intelligence
Practical solution techniques for first-order MDPs
Artificial Intelligence
Approximate inference for planning in stochastic relational worlds
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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
Action-space partitioning for planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Probabilistic planning via determinization in hindsight
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Probabilistic planning via heuristic forward search and weighted model counting
Journal of Artificial Intelligence Research
First order decision diagrams for relational MDPs
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
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
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Generalized first order decision diagrams for first order Markov decision processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning models of relational MDPs using graph kernels
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Incremental plan aggregation for generating policies in MDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Efficient learning of relational models for sequential decision making
Efficient learning of relational models for sequential decision making
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Probabilistic dialogue models with prior domain knowledge
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Active learning for teaching a robot grounded relational symbols
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually interpretable and they can be learned effectively from the action experiences in complex worlds. We investigate reasoning with such rules in grounded relational domains. Our algorithms exploit the compactness of rules for efficient and fexible decision-theoretic planning. As a first approach, we combine these rules with the Upper Confidence Bounds applied to Trees (UCT) algorithm based on look-ahead trees. Our second approach converts these rules into a structured dynamic Bayesian network representation and predicts the effects of action sequences using approximate inference and beliefs over world states. We evaluate the effectiveness of our approaches for planning in a simulated complex 3D robot manipulation scenario with an articulated manipulator and realistic physics and in domains of the probabilistic planning competition. Empirical results show that our methods can solve problems where existing methods fail.