Semantical and Computational Aspects of Horn Approximations (1993)
| Venue: | In Proceedings of the International Joint Conference of Artificial Intelligence |
| Citations: | 28 - 4 self |
BibTeX
@INPROCEEDINGS{Cadoli93semanticaland,
author = {Marco Cadoli},
title = {Semantical and Computational Aspects of Horn Approximations},
booktitle = {In Proceedings of the International Joint Conference of Artificial Intelligence},
year = {1993},
pages = {39--44}
}
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Abstract
In a recent study Selman and Kautz proposed a method, called Horn approximation, for speeding up inference in propositional Knowledge Bases. Their technique is based on the compilation of a propositional formula into a pair of Horn formulae: a Horn Greatest Lower Bound (GLB) and a Horn Least Upper Bound (LUB). In this paper we address two questions that have been only marginally addressed so far: 1) what is the semantics of the Horn approximations ? 2) what is the exact complexity of finding Horn approximations? We obtain semantical as well as computational results. The major results of the former kind are: Horn GLBs are closely related to models of the circumscription; reasoning wrt the Horn LUB can be mapped into classical reasoning. The major results of the latter kind are: finding a Horn GLB is "mildly" harder than solving the original inference problem; finding the Horn LUB is a search problem that cannot be parallelized. We believe that our results provide useful criteria that m...
