Principles of artificial intelligence
Principles of artificial intelligence
Computational complexity of terminological reasoning in BACK
Artificial Intelligence
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning action strategies for planning domains
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Reinforcement Learning
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Linear time near-optimal planning in the blocks world
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Evolutionary-based learning of generalised policies for AI planning domains
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Inference and Learning in Planning (Extended Abstract)
DS '09 Proceedings of the 12th International Conference on Discovery Science
Automatic induction of bellman-error features for probabilistic planning
Journal of Artificial Intelligence Research
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Scaling up heuristic planning with relational decision trees
Journal of Artificial Intelligence Research
A planner-based approach to generate and analyze minimal attack graph
Applied Intelligence
A universal planning system for hybrid domains
Applied Intelligence
Integrating relational reinforcement learning with reasoning about actions and change
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
A case-based approach to heuristic planning
Applied Intelligence
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In this paper we are concerned with the problem of learning how to solve planning problems in one domain given a number of solved instances. This problem is formulated as the problem of inferring a function that operates over all instances in the domain and maps states and goals into actions. We call such functions generalized policies and the question that we address is how to learn suitable representations of generalized policies from data. This question has been addressed recently by Roni Khardon (Technical Report TR-09-97, Harvard, 1997). Khardon represents generalized policies using an ordered list of existentially quantified rules that are inferred from a training set using a version of Rivest's learning algorithm (Machine Learning, vol. 2, no. 3, pp. 229–246, 1987). Here, we follow Khardon's approach but represent generalized policies in a different way using a concept language. We show through a number of experiments in the blocks-world that the concept language yields a better policy using a smaller set of examples and no background knowledge.