报告题目：Those who cannot remember the past are condemned to repeat it
报告时间：2025年12月16日（星期二）9：30
报告地点：海角社区
中心校区王湘浩楼A515报告厅
报 告 人：Peter J. Stuckey
报告人简介：
Peter J. Stuckey is a Professor in the Department of Data Science and Artificial Intelligence in the Faculty of Information Technology at Monash University. He received a B.Sc and Ph.D both in Computer Science from Monash University in 1985 and 1988 respectively. Since then he has worked at IBM T.J. Watson Research Labs, the University of Melbourne and Monash University. In 2009 he was recognized as an ACM Distinguished Scientist. In 2010 he was awarded the Google Australia Eureka Prize for Innovation in Computer Science for his work on lazy clause generation. In 2019 he was elected as an AAAI Fellow. and awarded the Association of Constraint Programming Award for Research Excellence. Peter Stuckey is a pioneer in constraint programming and logic programming. His research interests include: discrete optimization; programming languages, in particular declarative programing languages; constraint solving algorithms; path finding; bioinformatics; and neuro-symbolic AI; all relying on his expertise in symbolic and constraint reasoning. He enjoys problem solving in any area, having influential publications in e.g. databases, election science, system security, and timetabling, and working with companies such as Oracle and Rio Tinto on problems that interest them.
报告内容简介：
Constraint programming is a highly successful technology for tackling complex combinatorial optimization problems. Any form of combinatorial optimization involves some form of search, and CP is very well adapted to make use of programmed search and strong inference to solve some problems that are out of reach of competing technologies. But much of the search that happens during a CP execution is effectively repeated. This arises from the combinatorial nature of the problems we are tackling. Learning about past unsuccessful searches and remembering this in an effective way can exponentially reduce the size of the search space. In this talk I will explain lazy clause generation, which is a hybrid constraint solving technique that steals all the best learning ideas from Boolean satisfiability solvers, but retains all the advantages of constraint programming. Lazy clause generation provides the state of the art solutions to a wide range of problems, and consistently outperforms other solving approaches in the MiniZinc challenge. It is the architecture underlying the current bleeding edge CP solvers, and also required for auditable CP solvers, that create a verifiable proof log allowing us to certify their results.
主办单位：海角社区
计算机科学与技术学院
海角社区
海角社区
计算机科学技术研究所
符号计算与知识工程教育部重点实验室
仿真技术教育部重点实验室
网络技术及应用软件教育部工程研究中心
海角社区
国家级计算机实验教学示范中心
