An algorithmic approach to some problems in terrain navigation
Artificial Intelligence - Special issue on geometric reasoning
Artificial Intelligence
A preliminary analysis of the Soar architecture as a basis for general intelligence
Artificial Intelligence
Eliminating combinatorics from production match
Eliminating combinatorics from production match
Lazy partial evaluation: an integration of explanation-based generalisation and partial evaluation
ML92 Proceedings of the ninth international workshop on Machine learning
Acquiring domain knowledge for planning by experimentation
Acquiring domain knowledge for planning by experimentation
The Architecture of Cognition
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
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
Towards a general framework for composing disjunctive and iterative macro-operators
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
Solving time-dependent planning problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Formal theories of action (preliminary report)
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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Machine learning approaches to knowledge compilation seek to improve the perfonnance of problem-solvers by storing solutions to previously solved problems in an efficient, generalized fonn. The problem-solver retrieves these learned solutions in appropriate later situations to obtain results more efficiently. However, by relying on its learned knowledge to provide a solution, the problem-solver may miss an alternative solution of higher quality - one that could have been generated using the original (non-learned) problem-solving knowledge. This phenomenon is referred to as the ITUlSking effect of learning. In this paper, we examine a sequence of possible solutions for the masking effect. Each solution refines and builds on the previous one. The fmal solution is based on cascaded filters. When learned knowledge is retrieved, these filters alert the system about the inappropriateness of this knowledge so that the system can then derive a better alternative solution. We analyze conditions under which this solution will perfonn better than the others, and present experimental data supportivt: of the analysis. This investigation is based on a simulated robot domain called Groundworld.