Enforcing local consistencies is one of the main features of constraint reasoning. Which level of local consistency should be used when searching for solutions in a constraint network is a basic question. Arc consistency and partial forms of arc consistency have been widely studied, and have been known for sometime through the forward checking or the MAC search algorithms. Until recently, stronger forms of local consistency remained limited to those that change the structure of the constraint graph, and thus, could not be used in practice, especially on large networks. This paper focuses on the local consistencies that are stronger than arc consistency, without changing the structure of the network, i.e., only removing inconsistent values from the domains. In the last five years, several such local consistencies have been proposed by us or by others. We make an overview of all of them, and highlight some relations between them. We compare them both theoretically and experimentally, considering their pruning efficiency and the time required to enforce them.
School of Artificial Intelligence and Transportation Engineering, Fujian University of Technology（福建理工大学人工智能与交通工程学院）；Institute of Artificial Intelligence, Fujian University of Technology（福建理工大学人工智能研究院）
Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning
Northwestern Polytechnical University（西北工业大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shenzhen Research Institute of Northwestern Polytechnical University（西北工业大学深圳研究院）；Fudan University（复旦大学）；Harbin Institute of Technology（哈尔滨工业大学）；Tsinghua University（清华大学）；The Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
Domain Adaptive SAR Wake Detection: Leveraging Similarity Filtering and Memory Guidance
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center（青岛海洋科学与技术中心区域海洋学与数值模拟实验室）；School of Computer Science and Information Technology, The University of Nottingham（诺丁汉大学计算机科学与信息学院）；State Key Laboratory of Physical Oceanography, Department of Marine Technology, Ocean University of China（中国海洋大学物理海洋学国家重点实验室，海洋技术系）
DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
Tsinghua University（清华大学）；Stanford University（斯坦福大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Institute for Advanced Study（高等研究院）；Massachusetts Institute of Technology（麻省理工学院）
Stabilizing Open-Set Test-Time Adaptation via Primary-Auxiliary Filtering and Knowledge-Integrated Prediction
Department of Computer Science and Engineering, Sungkyunkwan University（计算机科学与工程系，成均馆大学）；Department of Artificial Intelligence, Sungkyunkwan University（人工智能系，成均馆大学）
