Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.
Learning Human-Humanoid Coordination for Collaborative Object Carrying
Department of Electrical and Electronic Engineering, the University of Hong Kong（香港大学电子与电气工程系）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Yuanpei College, Peking University（北京大学元培学院）
Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
Computer Vision and Robotics Laboratory (VeRLab), Department of Computer Science, Universidade Federal de Minas Gerais（计算机视觉与机器人实验室（VeRLab）、计算机科学系、巴西联邦大学矿务格里斯大学）
Hera: Learning Long-Horizon Coordination for Device-Cloud Collaborative LLM Agents
Fudan University（复旦大学）；Alibaba Group（阿里巴巴集团）；The University of Hong Kong（香港大学）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；New York University（纽约大学）；Universiti Malaya（马来亚大学）；SpaceAIC Co., Ltd.（SpaceAIC公司）
TruEyes: Utilizing Microtasks in Mobile Apps for Crowdsourced Labeling of Machine Learning Datasets
Center for Digital Technology and Management（数字技术与管理中心）；Technical University of Munich（慕尼黑工业大学）；Ludwig Maximilian University（路德维希-马克西米利安大学）；Bundeswehr University Munich（慕尼黑联邦国防军大学）
Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems
Beijing University of Technology（北京理工大学）；Zhejiang University（浙江大学）；ETH Zürich（苏黎世联邦理工学院）；Meituan LongCat Interaction Team（美团LongCat交互团队）；Vector Institute for Artificial Intelligence（人工智能向量研究所）
Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport
Department of Electrical Engineering, the University of Texas at Arlington（电气工程系，德克萨斯大学阿灵顿分校）；Department of Mechanical Engineering, Carnegie Mellon University（机械工程系，卡内基梅隆大学）
DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answering
Institute of Systems Engineering, Academy of Military Sciences（系统工程研究所，军事科学院）；School of Information Resource Management, Renmin University of China（信息资源管理学院，中国人民大学）
Towards a Collaborative Approach to Decision Making Based on Ontology and Multi-Agent System Application to crisis management
University of Manouba（马努巴大学）；National School of Computer Sciences（国立计算机科学学院）；University of Sousse（苏塞大学）；Higher Institute of Applied Science and Technology（应用科学与技术高等学院）
