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Generating Hard Satisfiability Problems (1996)
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| Venue: | Artificial Intelligence |
| Citations: | 109 - 2 self |
BibTeX
@ARTICLE{Selman96generatinghard,
author = {Bart Selman and David Mitchell and Hector Levesque},
title = {Generating Hard Satisfiability Problems},
journal = {Artificial Intelligence},
year = {1996},
volume = {81},
pages = {17--29}
}
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Abstract
We report results from large-scale experiments in satisfiability testing. As has been observed by others, testing the satisfiability of random formulas often appears surprisingly easy. Here we show that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which satisfiability testing is quite difficult. Our results provide a benchmark for the evaluation of satisfiability-testing procedures. In Artificial Intelligence, 81 (19996) 17--29. 1 Introduction Many computational tasks of interest to AI, to the extent that they can be precisely characterized at all, can be shown to be NP-hard in their most general form. However, there is fundamental disagreement, at least within the AI community, about the implications of this. It is claimed on the one hand that since the performance of algorithms designed to solve NP-hard tasks degrades rapidly with small increases in input size, something ...
Keyphrases
hard satisfiability problem satisfiability testing random formula general form ai community appropriate parameter value np-hard task degrades artificial intelligence small increase right distribution large-scale experiment input size satisfiability-testing procedure introduction many computational task fundamental disagreement
