Abstract
We present a characterization of heuristic static evaluation functions which unifies their treatment in single-agent problems and two-person games. The central thesis is that a useful heuristic function is one which is invariant along a solution path. This local characterization of heuristics can be used to predict the effectiveness of given heuristics and to automatically learn useful heuristic functions for problems.
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© 1986 Kluwer Academic Publishers
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Korf, R.E. (1986). Heuristics as Invariants and its Application to Learning. In: Machine Learning. The Kluwer International Series in Engineering and Computer Science, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2279-5_36
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DOI: https://doi.org/10.1007/978-1-4613-2279-5_36
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-9406-1
Online ISBN: 978-1-4613-2279-5
eBook Packages: Springer Book Archive