Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
An algorithm for probabilistic least-commitment planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Discovery as Autonomous Learning from the Environment
Machine Learning
Learning domain knowledge for teaching procedural skills
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Plan evaluation with incomplete action descriptions
Eighteenth national conference on Artificial intelligence
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
ARMS: an automatic knowledge engineering tool for learning action models for AI planning
The Knowledge Engineering Review
Learning Action Models with Quantified Conditional Effects for Software Requirement Specification
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Learning partially observable deterministic action models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Requirement specification based on action model learning
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
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
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Providing a complete and accurate domain model for an agent situated in a complex environment can be an extremely difficult task. Actions may have different effects depending on the context in which they are taken, and actions mayor may not induce their intended effects, with the probability of success again depending on context. We present an algorithm for automatically learning planning operators with context-dependent and probabilistic effects in environments where exogenous events change the state of the world. Empirical results show that the algorithm successfully finds operators that capture the true structure of an agent's interactions with its environment, and avoids spurious associations between actions and exogenous events.