Planning for conjunctive goals
Artificial Intelligence
Learning in intractable domains
Machine learning: a guide to current research
A general explanation-based learning mechanism and its application to narrative understanding
A general explanation-based learning mechanism and its application to narrative understanding
Learning by analyzing fortuitous occurrences
Proceedings of the sixth international workshop on Machine learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
A framework for modeling steady turning of robotic fish
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Incremental, approximate planning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Model-based diagnosis of planning failures
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Adaptive search by explanation-based learning of heuristic censors
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Learning general completable reactive plans
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Integrating abstraction and explanation-based learning in PRODIGY
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Explanation-based generalization of partially ordered plans
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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This paper describes an explanation-based approach lo learning plans despite a computationally intractable domain theory. In this approach, the system learns an initial plan using limited inference. In order to detect plans in which the limited inference causes a faulty plan the system monitors goal achievement in plan execution. When a plan unexpectedly fails to achieve a goal (or unexpectedly achieves the goal) a refinement process is triggered in which the system constructs an explanation for the expectation violation. This explanation is then used to refine the plan. By using expectation failures to guide search the learner avoids a computationally intractable exhaustive search involved in constructing a complete proof of the plan. This approach has the theoretical property of convergence upon a sound plan.