Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)
Massachusetts Institute of Technology（麻省理工学院）；Masdar Institute（马斯达尔理工学院）；Cardiff University（卡迪夫大学）；Université Paris-Dauphine（巴黎多芬大学）；PSL Research University（PSL研究大学）；CNRS（法国国家科学研究中心）；University of Luxembourg（卢森堡大学）
Edmond Awad, Martin Caminada, Gabriella Pigozzi, Mikołaj Podlaszewski, Iyad Rahwan
An inconsistent knowledge base can be abstracted as a set of arguments and a defeat relation among them. There can be more than one consistent way to evaluate such an argumentation graph. Collective argument evaluation is the problem of aggregating the opinions of multiple agents on how a given set of arguments should be evaluated. It is crucial not only to ensure that the outcome is logically consistent, but also satisfies measures of social optimality and immunity to strategic manipulation. This is because agents have their individual preferences about what the outcome ought to be. In the current paper, we analyze three previously introduced argument-based aggregation operators with respect to Pareto optimality and strategy proofness under different general classes of agent preferences. We highlight fundamental trade-offs between strategic manipulability and social optimality on one hand, and classical logical criteria on the other. Our results motivate further investigation into the relationship between social choice and argumentation theory. The results are also relevant for choosing an appropriate aggregation operator given the criteria that are considered more important, as well as the nature of agents' preferences.
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；China Land Surveying and Planning Institute（中国国土勘测规划院）；College of Architecture and Urban Planning, Tongji University（同济大学建筑与城市规划学院）；Chongqing Institute of Planning and Natural Resources Investigation and Monitoring（重庆市规划和自然资源调查监测院）；Li-Fi Research and Development Centre, University of Cambridge（剑桥大学Li-Fi研发中心）；Guangdong Urban-rural Planning and Design Research Institute Technology Group Co., Ltd.（广东省城乡规划设计研究院科技集团股份有限公司）；Engineering Research Center of Key Software Technologies for Smart City Perception and Planning, Ministry of Education（智慧城市感知与规划关键软件技术教育部工程研究中心）
Locally Pareto-Optimal Interpretations for Black-Box Machine Learning Models
University of California at Berkeley（加州大学伯克利分校）；Indian Institute of Technology Bombay（印度班加罗尔理工学院）；Chalmers University of Technology and University of Gothenburg（查尔姆斯理工大学和哥德堡大学）
