The Pseudo-Boolean Optimization (PBO) and Maximum Satisfiability (MaxSAT) problems are natural optimization extensions of Boolean Satisfiability (SAT).
In the recent past, different algorithms have been proposed for PBO and for MaxSAT, despite the existence of straightforward mappings from PBO to MaxSAT and vice-versa. This papers proposes Weighted Boolean Optimization (WBO), a new unified framework that aggregates and extends PBO and MaxSAT. In addition, the paper proposes a new unsatisfiability-based algorithm for WBO, based on recent unsatisfiability-based algorithms for MaxSAT. Besides standard MaxSAT, the new algorithm can also be used to solve weighted MaxSAT and PBO, handling pseudo-Boolean constraints either natively or by translation to clausal form. Experimental results illustrate that unsatisfiability-based algorithms for MaxSAT can be orders of magnitude more efficient than existing dedicated algorithms. Finally, the paper illustrates how other algorithms for either PBO or MaxSAT can be extended to WBO.
Towards Projected and Incremental Pseudo-Boolean Model Counting
Grabtaxi Holdings（Grab出租车控股公司）；Grab-NUS AI Lab（Grab-新加坡国立大学人工智能实验室）；Georgia Institute of Technology（佐治亚理工学院）；National University of Singapore（新加坡国立大学）；University of Toronto（多伦多大学）
Runtime Analysis of Evolutionary Algorithms for Multi-party Multi-objective Optimization
School of Artificial Intelligence, Nanjing University of Information Science and Technology（人工智能学院，南京信息工程大学）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology（新型安全智能技术广东省重点实验室，网络空间安全学院，计算机科学与技术学院，哈尔滨工业大学）
