Random Constraint Satisfaction: Theory Meets Practice

Abstract. We study the experimental consequences of a recent theoretical result by Achlioptas et al. that shows that conventional models of random problems are trivially insoluble in the limit. We survey the literature to identify experimental studies that lie within the scope of this result. We then estimate theoretically and measure experimentally the size at which problems start to become trivially insoluble. Our results demonstrate that most (but not all) of these experimental studies are luckily una ected by this result. We also study an alternative model of random problems that does not su er from this asymptotic weakness. We show that, at a typical problem size used in experimental studies, this model looks similar to conventional models. Finally, we generalize this model so that we can independently adjust the constraint tightness and density.

• Type: Conference
• Year: 1998
• Where: CP
• Authors: Ewan MacIntyre, Patrick Prosser, Barbara M. Smith, Toby Walsh
• Keywords: Artificial Intelligence, Conventional Models, CP 1998, Random Problems, Trivially