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Summary: Journal of Artificial Intelligence Research 24 (2005) 623-639 Submitted 12/04; published 11/05
Hiding Satisfying Assignments: Two are Better than One
Dimitris Achlioptas optas@microsoft.com
Microsoft Research
Redmond, Washington
Haixia Jia hjia@cs.unm.edu
Computer Science Department
University of New Mexico
Cristopher Moore moore@cs.unm.edu
Computer Science Department
University of New Mexico
Abstract
The evaluation of incomplete satisfiability solvers depends critically on the availability
of hard satisfiable instances. A plausible source of such instances consists of random k-
SAT formulas whose clauses are chosen uniformly from among all clauses satisfying some
randomly chosen truth assignment A. Unfortunately, instances generated in this manner
tend to be relatively easy and can be solved efficiently by practical heuristics. Roughly
speaking, for a number of different algorithms, A acts as a stronger and stronger attractor
as the formula's density increases. Motivated by recent results on the geometry of the space
of satisfying truth assignments of random k-SAT and NAE-k-SAT formulas, we introduce
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