We show how coupling of local optimization processes can lead to better solutions than multi-start local optimization consisting of independent runs. This is achieved by minimizing the average energy cost of the ensemble, subject to synchronization constraints between the state vectors of the individual local minimizers. From an augmented Lagrangian which incorporates the synchronization constraints both as soft and hard constraints, a network is derived wherein the local minimizers interact and exchange information through the synchronization constraints. From the viewpoint of neural networks, the array can be considered as a Lagrange programming network for continuous optimization and as a cellular neural network (CNN). The penalty weights associated with the soft state synchronization constraints follow from the solution to a linear program. This expresses that the energy cost of the ensemble should maximally decrease. In this way successful local minimizers can implicitly impose their state to the others through a mechanism of master-slave dynamics resulting into a cooperative search mechanism. Improved information spreading within the ensemble is obtained by applying the concept of small-world networks. This work suggests, in an interdisciplinary context, the importance of information exchange and state synchronization within ensembles, towards issues as evolution, collective behaviour, optimality and intelligence.
Cooperative Resilience in Artificial Intelligence Multiagent Systems
Universidad de los Andes（安第斯大学）；Center of Excellence in Analytics and Artificial Intelligence Bancolombia（哥伦比亚银行分析与人工智能卓越中心）；Universidad de Ibagué（伊瓦格大学）
Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020)
TriRhenaTech联盟展示了计划于2020年5月13日在卡尔斯鲁厄举行的取消的三国“上莱茵人工智能研讨会”的论文集。TriRhenaTech联盟是上莱茵河三国大都市区的大学网络，包括位于富特旺根、凯泽斯鲁滕、卡尔斯鲁厄和奥芬堡的德国应用科学大学，巴登-符腾堡州合作州立大学Loerrach，法国大学网络阿尔萨斯理工大学，瑞士国立大学（由14所工程、建筑和管理领域的“高等院校”组成）和瑞士西北应用科学与艺术大学（University of Applied Sciences and Arts Northwestern Switzerland）。该联盟的共同目标是加强知识、研究和技术的转移，以及学生的跨境流动。
Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification
Faculty of Engineering & Information Technology, University of Technology Sydney（工程与信息科技学院，技术悉尼大学）；Department of Civil and Architectural Engineering, Sultan Qaboos University（土木与建筑工程系，苏丹·卡布斯大学）；University Research and Innovation Center (EKIK), Obuda University（研究与创新中心（EKIK），奥布达大学）
Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge
Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；University of California, Santa Barbara（加州大学圣芭芭拉分校）；Harvard University（哈佛大学）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）；MIT（麻省理工学院）；Johns Hopkins University（约翰霍普金斯大学）；Honda Research Institute USA（本田美国研究院）
Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems
University of Vaasa（瓦萨大学）；University of Arts London（伦敦艺术大学）；University of Jyväskylä（于韦斯屈莱大学）；University of Helsinki（赫尔辛基大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）；University of Tampere（坦佩雷大学）
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Institute of Technology（北京理工大学）；Xi’an Jiaotong University（西安交通大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Graphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization
School of Mathematical Sciences, East China Normal University（华东师范大学数学科学学院）；Key Laboratory of Management, Decision and Information Systems, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院管理决策与信息系统重点实验室）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）；School of Automation, China University of Geosciences（中国地质大学（武汉）自动化学院）
