Abstract
This paper presents the Optimal
Tunneling heuristic for learning macro operators. Optimal
Tunneling produces shorter more useful macros than the similar Minimum
to Minimum heuristic presented by Iba. Optimal Tunneling is arguably
an improvement since its macros (a) best reduce search cost, (b) give
the most accurate modification to the search space to make the
heuristic function correct, and (c) result in better performance on
comparative tests. Optimal Tunneling creates macros that cross
exactly the expensive segment of the heuristic function along the
current solution path. A water pouring analogy is proposed to
illustrate the effect of macros on the cost of search in problem
solving.
Notes
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