Acquisition of abstract plan descriptions for plan recognition
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
WoLLIC '09 Proceedings of the 16th International Workshop on Logic, Language, Information and Computation
The role of macros in tractable planning
Journal of Artificial Intelligence Research
The Knowledge Engineering Review
Defining operationality for explanation-based learning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
PROLEARN: towards a prolog interpreter that learns
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
An architecture for intelligent task automation
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Implicit Learning of Compiled Macro-Actions for Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Defining operationality for explanation-based learning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
PROLEARN: towards a prolog interpreter that learns
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
An architecture for intelligent task automation
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Learning general completable reactive plans
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
COMPOSER: a probabilistic solution to the utility problem in speed-up learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Forming concepts for fast inference
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Inductive rule learning on the knowledge level
Cognitive Systems Research
| Hi-index | 0.00 |
Problem solving programs that generalize and save plans in order to improve their subsequent performance inevitably face the danger of being overwhelmed by an ever-increasing number of stored plans. To cope with this problem, methods must be developed for selectively learning only the most valuable aspects of a new plan. This paper describes MORRIS, a heuristic problem solver that measures the utility of plan fragments to determine whether they are worth learning. MORRIS generalizes and saves plan fragments if they are frequently used, or if they are helpful in solving difficult subproblems. Experiments are described comparing the performance of MORRIS to a less selective learning system.