Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each other besides a counter offer. This can lead to unnecessarily long negotiations when, for example, negotiations are impossible, risking to waste bandwidth that is a precious resource at the edge of the network. While alternative protocols exist which alleviate this problem, these solutions are too complex for low power devices, such as IoT sensors operating at the edge of the network. To improve this bottleneck, we introduce an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in which agents can also express constraints to each other. This allows agents to both search the possibility space more efficiently and recognise impossible situations sooner. We empirically show that agents using ACOP can significantly reduce the number of messages a negotiation takes, independently of the strategy agents choose. In particular, we show our method significantly reduces the number of messages when an agreement is not possible. Furthermore, when an agreement is possible it reaches this agreement sooner with no negative effect on the utility.
The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets
Stanford University（斯坦福大学）；University of Toronto（多伦多大学）；Google DeepMind（谷歌DeepMind）；University of Southern California（南加州大学）；Massachusetts Institute of Technology（麻省理工学院）
ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation
Department of Computer Science and Communications Engineering, Waseda University（早稻田大学计算机科学与通信工程系）；College of Architecture and Urban Planning, Tongji University（同济大学建筑与城市规划学院）；Advanced Institute of So-Go-Chi (Convergence Knowledge) Informatics, Tohoku University（东北大学So-Go-Chi（融合知识）信息学高级研究所）；Aisin Corporation（日产公司）
A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks
i2CAT Foundation（i2CAT基金会）；Hostelworld Group（Hostelworld集团）；Technical University of Catalonia (UPC)（技术大学（加泰罗尼亚））；Khalifa University of Science and Technology（卡里玛大学）；ISI/ATH；University of Patras（帕特拉大学）
SCARCE: Emergent Institutions in Long-Horizon Multi-Agent Coding Systems
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；ELLIS Institute Tübingen（蒂宾根 ELLS 研究所）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（蒂宾根人工智能中心）
Agentic TinyML for Intent-aware Handover in 6G Wireless Networks
Center for Ubiquitous Computing（无处不在计算中心）；University of Oulu（奥卢大学）；Department of Communication Systems（通信系统系）；EURECOM；Department of Computer Science（计算机科学系）；University of Helsinki（赫尔辛基大学）；RISE Research Institutes of Sweden（瑞典研究机构）；Department of Computer Science, Electrical and Space Engineering（计算机科学、电气与空间工程系）；Luleå University of Technology（卢勒奥技术大学）
