TY - GEN
T1 - Refutation by randomised general resolution
AU - Prestwich, Steven
AU - Lynce, Inês
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/36348972239
M3 - Conference proceeding
AN - SCOPUS:36348972239
SN - 1577353234
SN - 9781577353232
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 1667
EP - 1670
BT - AAAI-07/IAAI-07 Proceedings
T2 - AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Y2 - 22 July 2007 through 26 July 2007
ER -