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Title:
Double configuration checking in stochastic local search for satisfiability
Author: Luo, Chuan (1) ; Cai, Shaowei (2) ; Wu, Wei (1) ; Su, Kaile (1)
Conference Name: 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Conference Date: July 27, 2014 - July 31, 2014
Issued Date: 2014
Conference Place: Quebec City, QC, Canada
Corresponding Author: Cai, Shaowei
Publish Place: AI Access Foundation
Indexed Type: EI
ISBN: 9781577356806
Department: (1) Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University, Beijing, China; (2) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; (3) Queensland Research Laboratory, NICTA, Brisbane, Australia; (4) Australia institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia
Abstract: Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way. We use the DCCA heuristic to design an efficient SLS solver for SAT dubbed DCCASat. The experiments show that the DCCASat solver significantly outperforms a number of state-of-the-art solvers on ex-tensive random k-SAT benchmarks at the phase transition. Moreover, DCCASat shows good performance on structured benchmarks, and a combination of DCCASat with a complete solver achieves state-of-the-art performance on structured benchmarks.
English Abstract: Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way. We use the DCCA heuristic to design an efficient SLS solver for SAT dubbed DCCASat. The experiments show that the DCCASat solver significantly outperforms a number of state-of-the-art solvers on ex-tensive random k-SAT benchmarks at the phase transition. Moreover, DCCASat shows good performance on structured benchmarks, and a combination of DCCASat with a complete solver achieves state-of-the-art performance on structured benchmarks.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16609
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Luo, Chuan ,Cai, Shaowei ,Wu, Wei ,et al. Double configuration checking in stochastic local search for satisfiability[C]. 见:28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014. Quebec City, QC, Canada. July 27, 2014 - July 31, 2014.
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