Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning action models for reactive autonomous agents
Learning action models for reactive autonomous agents
Automatically labeling the inputs and outputs of web services
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A planning approach for message-oriented semantic web service composition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Learning semantic definitions of online information sources
Journal of Artificial Intelligence Research
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Incremental learning of relational action models in noisy environments
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Learning action models for multi-agent planning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Integrating task planning and interactive learning for robots to work in human environments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Learning web-service task descriptions from traces
Web Intelligence and Agent Systems
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This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages--traditional STRIPS, and a new language STRIPS+WS that extends STRIPS to allow for the creation of new objects when an action is executed. This modification allows STRIPS+WS to model web services and can be used to describe web-service composition (planning) problems. We show that general STRIPS operators cannot be efficiently learned through raw experience, though restricting the size of action preconditions yields a positive result. We then show that efficient learning is possible without this restriction if an agent has access to a "teacher" that can provide solution traces on demand. We adapt this learning algorithm to efficiently learn web-service descriptions in STRIPS+WS.