Learning to solve problems by searching for macro-operators
Learning to solve problems by searching for macro-operators
Logic for problem-solving
Information filters and their implementation in the SYLLOG system
Proceedings of the sixth international workshop on Machine learning
Some studies in machine learning using the game of checkers
Computers & thought
A Simplified Format for the Model Elimination Theorem-Proving Procedure
Journal of the ACM (JACM)
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
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
Empirical analysis of the general utility problem in machine learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
| Hi-index | 0.00 |
This paper highlights a phenomenon that causes deductively learned knowledge to be harmful when used for problem solving. The problem occurs when deductive problem solvers encounter a failure branch of the search tree. The backtracking mechanism of such problem solvers will force the program to traverse the whole subtree thus visiting many nodes twice - once by using the deductively learned rule and once by using the rules that generated the learned rule in the first place. We suggest an approach called utilization filtering to solve that problem. Learners that use this approach submit to the problem solver a filter function together with the knowledge that was acquired. The function decides for each problem whether to use the learned knowledge and what part of it to use. We have tested the idea in the context of a lemma learning system, where the filter uses the probability of a subgoal failing to decide whether to turn lemma usage off. Experiments show an improvement of performance by a factor of 3.