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.
