Principles of artificial intelligence
Principles of artificial intelligence
Transfer of training in procedural learning: a matter of conjectures and refutations?
Computational models of learning
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Learning by experimentation: the operator refinement method
Machine learning
Why EBL produces overly-specific knowledge: a critique of the PRODIGY approaches
ML92 Proceedings of the ninth international workshop on Machine learning
Acquiring search-control knowledge via static analysis
Artificial Intelligence
Small is beautiful: a brute-force approach to learning first-order formulas
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Incremental learning of control knowledge for nonlinear problem solving
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Prosodic aids to syntactic and semantic analysis of spoken English
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
The effect of rule use on the utility of explanation-based learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Eliminating expensive chunks by restricting expressiveness
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Effective generalization of relational descriptions
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Multi-strategy learning of search control for partial-order planning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Scaling up heuristic planning with relational decision trees
Journal of Artificial Intelligence Research
| Hi-index | 0.01 |
One method for reducing the time required for plan generation is to learn search control rules from experience. The most common approach to learning search control knowledge has been explanationbased learning. An alternative approach is to use inductive learning. An inductive approach does not require a complete and tractable domain theory and has the potential to create more effective rules by learning from more than one example at a time. In this paper we describe Grasshopper, an inductive system that learns search control rules for a classical plan generation system. We also provide an empirical evaluation of Grasshopper by comparing it with an existing explanation-based learning system.