Probabilistically Estimating Backbones and Variable Bias: Experimental Overview

Backbone variables have the same assignment in all solutions to a given constraint satisfaction problem; more generally, bias represents the proportion of solutions that assign a variable a particular value. Intuitively such constructs would seem important to efficient search, but their study to date has been from a mostly conceptual perspective, in terms of indicating problem hardness or motivating and interpreting heuristics. Here we summarize a two-phase project where we first measure the ability of both existing and novel probabilistic message-passing techniques to directly estimate bias and identify backbones for the Boolean Satisfiability (SAT) Problem. We confirm that methods like Belief Propagation and Survey Propagation

• Type: Conference
• Year: 2008
• Where: CP
• Authors: Eric I. Hsu, Christian J. Muise, J. Christopher Beck, Sheila A. McIlraith
• Keywords: Artificial Intelligence, Bias, Constraint Satisfaction Problem, CP 2008, Probabilistic Message-passing Techniques