Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning partially observable deterministic action models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
Learning partially observable action models: efficient algorithms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Refining the execution of abstract actions with learned action models
Journal of Artificial Intelligence Research
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
On estimating actuation delays in elastic computing systems
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
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We present an algorithm that derives actions' effects and preconditions in partially observable, relational domains. Our algorithm has two unique features: an expressive relational language, and an exact tractable computation. An action-schema language that we present permits learning of preconditions and effects that include implicit objects and unstated relationships between objects. For example, we can learn that replacing a blown fuse turns on all the lights whose switch is set to on. The algorithm maintains and outputs a relational-logical representation of all possible action-schema models after a sequence of executed actions and partial observations. Importantly, our algorithm takes polynomial time in the number of time steps and predicates. Time dependence on other domain parameters varies with the action-schema language. Our experiments show that the relational structure speeds up both learning and generalization, and outperforms propositional learning methods. It also allows establishing apriori-unknown connections between objects (e.g. light bulbs and their switches), and permits learning conditional effects in realistic and complex situations. Our algorithm takes advantage of a DAG structure that can be updated efficiently and preserves compactness of representation.