Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Instance-Based Learning Algorithms
Machine Learning
Machine learning: a theoretical approach
Machine learning: a theoretical approach
Learning to plan in continuous domains
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Efficient exploration for optimizing immediate reward
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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We advance a knowledge-based learning method that augments conventional generalization to permit concept acquisition in failure domains. These are domains in which learning must proceed exclusively with failure examples that are relatively uninformative for conventional methods. A domain theory is used to explain and then systematically perturb the observed failures so that they can be treated as if they were positive training examples. The concept induced from these "phantom" examples is exercised in the world, yielding additional observations, and the process repeats. Surprisingly, an accurate concept can often be learned even if the phantom examples are themselves failures and the domain theory is only imprecise and approximate. We investigate the behavior of the method in a stylized air-hockey domain which demands a nonlinear decision concept. Learning is shown empirically to be robust in the face of degraded domain knowledge. An interpretation is advanced which indicates that the information available from a plausible qualitative domain theory is sufficient for robust successful learning.