Trust-based Multiagent Consensus or Weightings Aggregation

From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems

The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.

BRNES：在多智能体机器人与自主系统中实现安全与隐私感知的经验共享

BRNES: Enabling Security and Privacy-aware Experience Sharing in Multiagent Robotic and Autonomous Systems

提出BRNES多智能体强化学习框架，通过动态邻居选择、加权经验聚合和本地差分隐私噪声，抵御拜占庭攻击与推断攻击，实现安全且隐私感知的经验共享，性能显著优于现有方法。

Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management

In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. We focus on demonstrating how clustering of similar users enables a critical new functionality: supporting more personalized, and thus more accurate predictions for users. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context…

迈向满意型分布式动态团队信任的步骤

Steps Towards Satisficing Distributed Dynamic Team Trust

本文面向动态人机团队，提出从目标、个人/团队价值观和法律原则刻画信任，并设计可解释指标与模拟任务实验以验证“满意型信任”。

通过 ChatGPT 从合同中提取规范：机遇与挑战

Extracting Norms from Contracts Via ChatGPT: Opportunities and Challenges

本文评估 ChatGPT 从合同中提取承诺、禁止、授权、权力及相关要素的能力，验证其零样本潜力并指出导致错误的主要局限。

显式特征推断用于多智能体协调

Explicit Trait Inference for Multi-Agent Coordination

本文提出显式特征推断方法，通过推断伙伴特征提升多智能体协调效率，在经济游戏和复杂多智能体场景中均取得显著效果。