QQ客服 官方微博 反馈留言

	Tailoring local search for partial MaxSAT
	Cai, Shaowei (1) ; Luo, Chuan (3) ; Thornton, John (4) ; Su, Kaile (4)
	2014
会议名称	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
会议日期	July 27, 2014 - July 31, 2014
会议地点	Quebec City, QC, Canada
页码	2623-2629
通讯作者	Cai, Shaowei
中文摘要	Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called Dist. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that Dist significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial benchmark, Dist dramatically outperforms previous local search algorithms and is comparable with complete algorithms.
英文摘要	Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called Dist. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that Dist significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial benchmark, Dist dramatically outperforms previous local search algorithms and is comparable with complete algorithms.
收录类别	EI
会议录出版地	AI Access Foundation
语种	英语
ISBN号	9781577356806
内容类型	会议论文
源URL	[http://ir.iscas.ac.cn/handle/311060/16611]
专题	软件研究所_软件所图书馆_会议论文
推荐引用方式 GB/T 7714	Cai, Shaowei ,Luo, Chuan ,Thornton, John ,et al. Tailoring local search for partial MaxSAT[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.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。