Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Using and refining simplifications: explanation-based learning of plans in intractable domains
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Lazy explanation-based learning: a solution to the intractable theory problem
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Failsafe: a floor planner that uses EBG to learn from its failures
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
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We introduce an adaptive search technique that speeds up state space search by learning heuristic censors while searching. The censors speed up search by pruning away moTe and more of the space until a solution is found in the pruned space. Censors are learned by explaining dead ends and other search failures. To learn quickly, the technique over-generalizes by assuming that certain constraints aTe preservable, i.e., remain true on at least one solution path. A recovery mechanism detects violations of this assumption and selectively relaxes learned censors. The technique, implemented in an adaptive problem solver named FAILSAFE-2, learns useful heuristics that cannot be learned by other reported methods. Its effectiveness is indicated by a preliminary complexity analysis and by experimental results in three domains, including one in which PRODIGY failed to learn eflective search control rules.