Learning action strategies for planning domains
Artificial Intelligence
Stochastic dynamic programming with factored representations
Artificial Intelligence
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Symbolic heuristic search for factored Markov decision processes
Eighteenth national conference on Artificial intelligence
Logic and Learning
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Inductive policy selection for first-order MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Implementation and comparison of solution methods for decision processes with non-markovian rewards
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Practical solution techniques for first-order MDPs
Artificial Intelligence
Adaptive Multi-Agent Programming in GTGolog
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Journal of Artificial Intelligence Research
FLUCAP: a heuristic search planner for first-order MDPs
Journal of Artificial Intelligence Research
First order decision diagrams for relational MDPs
Journal of Artificial Intelligence Research
ReTrASE: integrating paradigms for approximate probabilistic planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Adaptive multi-agent programming in GTGolog
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Automatic induction of bellman-error features for probabilistic planning
Journal of Artificial Intelligence Research
Planning with noisy probabilistic relational rules
Journal of Artificial Intelligence Research
Declarative programming for agent applications
Autonomous Agents and Multi-Agent Systems
Probabilistic relational planning with first order decision diagrams
Journal of Artificial Intelligence Research
Imitation learning in relational domains: a functional-gradient boosting approach
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Monitoring the execution of partial-order plans via regression
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Discovering hidden structure in factored MDPs
Artificial Intelligence
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We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function using first-order decision-theoretic regression and formula rewriting, while the former, when provided with a suitable hypotheses language, are capable of generalising value functions or policies for small instances. Our idea is to use reasoning and in particular classical first-order regression to automatically generate a hypotheses language dedicated to the domain at hand, which is then used as input by an inductive solver. This approach avoids the more complex reasoning of symbolic dynamic programming while focusing the inductive solver's attention on concepts that are specifically relevant to the optimal value function for the domain considered.