Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Learning to solve problems by searching for macro-operators
Learning to solve problems by searching for macro-operators
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
COLT '89 Proceedings of the second annual workshop on Computational learning theory
The (n2-1)-puzzle and related relocation problems
Journal of Symbolic Computation
A Critical Look at Experimental Evaluations of EBL
Machine Learning
Artificial Intelligence
Measuring utility and the design of provably good EBL algorithms
ML92 Proceedings of the ninth international workshop on Machine learning
Linear-space best-first search
Artificial Intelligence
Information Filtering: Selection Mechanisms in Learning Systems
Machine Learning
Statistical Methods for Analyzing Speedup Learning Experiments
Machine Learning
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Learning Goal-Decomposition Rules using Exercises
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A formal framework for speedup learning from problems and solutions
Journal of Artificial Intelligence Research
Finding optimal solutions to the twenty-four puzzle
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Speedup learning for repair-based search by identifying redundant steps
The Journal of Machine Learning Research
Learning and applying competitive strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
Learning heuristic functions for large state spaces
Artificial Intelligence
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
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One of the most common mechanisms used for speeding up problem solvers is macro-learning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macro-learning presents is the vast number of macros that are available for acquisition. Macros increase the branching factor of the search space and can severely degrade problem-solving efficiency. To make macro learning useful, a program must be selective in acquiring and utilizing macros. This paper describes a general method for selective acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of N × N sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.