Abstract
In this paper we presented FAILSAFE-II, a system that performs adaptive search by learning on-line form its failures. The key contribution of this system is its use of the preservability of failures. Preservability assumption allows FAILSAFE-II to over-generalize the failures and discard some solutions to the problem along with the non-solutions. This leads to learning of search control rules which could not be learned by systems like PRODIGY [4], STATIC [2] or the other systems that learn from search failures. We demonstrated FAILSAFE-II's performance improvement in three domains including one in which PRODIGY failed to learn effective control rules.
This paper originally appeared in a modified form in Proceedings of the Eighth National Conference on AI, July 29–August 3, 1990, AAAI Press/MIT Press.
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© 1993 Springer-Verlag Berlin Heidelberg
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Bhatnagar, N. (1993). Adaptive search by learning from incomplete explanations of failures. In: Hanson, S.J., Remmele, W., Rivest, R.L. (eds) Machine Learning: From Theory to Applications. Lecture Notes in Computer Science, vol 661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56483-7_24
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DOI: https://doi.org/10.1007/3-540-56483-7_24
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