Refutation by Randomised General Resolution

Local search is widely applied to satisfiable SAT problems, and on some problem classes outperforms backtrack search. An intriguing challenge posed by Selman, Kautz and McAllester in 1997 is to use it instead to prove unsatisfiability. We design a greedy randomised resolution algorithm called RANGER that will eventually refute any unsatisfiable instance while using only bounded memory. RANGER can refute some problems more quickly than systematic resolution or backtracking with clause learning. We believe that non-systematic but greedy inference is an interesting research direction for powerful proof systems such as general resolution.

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Steven David Prestwich, Inês Lynce
• Keywords: AAAI 2007, Classes Outperforms Backtrack, Greedy Randomised Resolution, Intelligent Agents, Satisfiable Sat Problems