Weight Redistribution for Unweighted MAX-SAT

Many real-world problems are over-constrained and require search techniques adapted to optimising cost functions rather than searching for consistency. This makes the MAX-SAT problem an important area of research for the satisfiability (SAT) community. In this study we perform an empirical analysis of several of the best performing SAT local search techniques in the domain of unweighted MAX-SAT. In particular, we test two of the most recently developed SAT clause weight redistribution algorithms, DDFW and DDFW+ , against three more well-known techniques (RSAPS, AdaptNovelty+ and PAWS). Based on an empirical study across a range of previously studied problems we conclude that DDFW is the most promising algorithm in terms of robust average performance.

• Type: Conference
• Year: 2007
• Where: AUSAI
• Authors: Abdelraouf Ishtaiwi, John Thornton, Abdul Sattar
• Keywords: Artificial Intelligence, AUSAI 2007, Many Real-world Problems, SAT Clause Weight, Search Techniques