Hillary is a general learning system that
selectivly acquire macro-operators for speeding up search programs. 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 was 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. Hillary was developed by Lev Finkelshtein and
Shaul Markovitch.
- A paper
in JAIR(the Journal of Artificial Intelligence Research) describing
Hillary and experiments performed with it.
- A simple description of
Hillary for the layman.
- A JAVA demo
of Hillary solving puzzles.
- Source code for Hillary in Common Lisp:
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Designed by Ofer Avnery
& Eyal Fingold
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