Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Proceedings of the sixth international workshop on Machine learning
Combining empirical and analytical learning with version spaces
Proceedings of the sixth international workshop on Machine learning
Finding new rules for incomplete theories: explicit biases for induction with contextual information
Proceedings of the sixth international workshop on Machine learning
A Heuristic Approach to the Discovery of Macro-Operators
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
The use of multiple problem decompositions in time constrained planning tasks
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Constraint satisfaction with delayed evaluation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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This paper describes an application of an analytical learning technique, Plausible Explanation-Based Learning (PEBL), that dynamically acquires search control knowledge for a constraint-based scheduling system. In general, the efficiency of a scheduling system suffers because of resource contention among activities. Our system learns the general conditions under which chronic contention occurs and uses search control to avoid repeating mistakes. Because it is impossible to prove that a chronic contention will occur with only one example, traditional EBL techniques are insufficient. We extend classical EBL by adding an empirical component that creates search control rules only when the system gains enough confidence in the plausible explanations. This extension to EBL was driven by our observations about the behavior of our scheduling system when applied to the real-world problem of scheduling tasks for NASA Space Shuttle payload processing. We demonstrate the utility of this approach and provide experimental results.