Journal of Artificial Intelligence Research 8 (1998), pp. 223-263. Submitted 11/97; published 6/98
© 1998 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.
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A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle

Lev Finkelstein []
IBM - Haifa Research Laboratory,
Matam, Haifa, Israel
- Shaul Markovitch []
Computer Science Department,
Technion, Haifa 32000, Israel


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 NxN sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.

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Shaul Markovitch