Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
A framework of simplifications in learning to plan
Proceedings of the first international conference on Artificial intelligence planning systems
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
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
Selectively generalizing plans for problem-solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Tradeoffs in the empirical evaluation of competing algorithm designs
Annals of Mathematics and Artificial Intelligence
Probabilistic exploration in planning while learning
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Sequential model-based optimization for general algorithm configuration
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
| Hi-index | 0.01 |
In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the COMPOSER system which embodies a probabilistic solution to the utility problem. We outline the statistical foundations of our approach and compare it against four other approaches which appear in the literature.