Towards Adaptive, Scalable, and Robust Coordination of LLM Agents: A Dynamic Ad-Hoc Networking Perspective
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）；Huawei Technologies Ltd.（华为技术有限公司）；Beijing University of Posts and Telecommunications（北京邮电大学）
Multi-agent architectures built on large language models (LLMs) have demonstrated the potential to realize swarm intelligence through well-crafted collaboration. However, the substantial burden of manual orchestration inherently raises an imperative to automate the design of agentic workflows. We frame such an agent coordination challenge as a classic problem in dynamic ad-hoc networking: How to establish adaptive and reliable communication among a scalable number of agentic hosts? In response to this unresolved dilemma, we introduce RAPS, a reputation-aware publish-subscribe paradigm for adaptive, scalable, and robust coordination of LLM agents. RAPS is grounded in the Distributed Publish-Subscribe Protocol, allowing LLM agents to exchange messages based on their declared intents rather than predefined topologies. Beyond this substrate, RAPS further incorporates two coherent overlays: (i) Reactive Subscription, enabling agents to dynamically refine their intents; and (ii) Bayesian Reputation, empowering each agent with a local watchdog to detect and isolate malicious peers. Extensive experiments over five benchmarks showcase that our design effectively reconciles adaptivity, scalability, and robustness in a unified multi-agent coordination framework.
A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems
University of Edinburgh（爱丁堡大学）
ad hoc协调问题是指在没有事先协调机制的多智能体系统中设计一个能够实现最佳灵活性和效率的自主智能体。我们概念化这个问题正式使用博弈论模型，称为随机贝叶斯博弈，其中的行为的球员是由其私人信息，或类型。基于这个模型，我们推导出一个解决方案，称为Harsanyi-Bellman Ad Hoc Coordination（HBA），它利用贝叶斯纳什均衡的概念，在规划过程中找到贝尔曼最优控制意义上的最佳行动。我们评估HBA在多智能体物流领域称为基于水平的觅食，它实现了更高的灵活性和效率比几种替代算法。我们还报告了一个公共科学展览中的人机实验，其中人类参与者反复扮演囚徒困境和石头剪刀布对HBA和替代算法，表明HBA实现了相同的效率和显着更高的福利和胜率。
Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore…
Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achieve this goal, demonstrating success in a series of challenging problems. However, whilst these advances are significant, the vast majority of focus has been on the self-play paradigm. This often results in a coordination problem, caused by agents learning to make use of arbitrary conventions when playing with themselves. This means that even the strongest self-play agents may ha…
Bar Ilan University（巴伊兰大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of Edinburgh（爱丁堡大学）；The University of Birmingham（伯明翰大学）；Sony AI（索尼人工智能）
Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination. This survey makes a two-fold contribution: First, it provides a structured description of the different facets of the ad hoc teamwork problem. Second, it discusses the progress that has been made in the field so far, and identifies the immediate and long-term open problems that need to be addressed in ad hoc teamwork.
SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking
1 School of Elect. Inform. \& Commun., Huazhong Univ. of Science \& Technology, China；2 Peng Cheng Laboratory, Shenzhen, China；3 Pazhou Laboratory (Huangpu), Guangzhou, China；4 Department of Electrical；Computer Engineering, the University of Arizona
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
C$^{2}$DL, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所C²DL实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；University of Science and Technology of China（中国科学技术大学）；Qwen Team, Alibaba Group（阿里集团Qwen团队）
We present an architecture for ad hoc teamwork, which refers to collaboration in a team of agents without prior coordination. State of the art methods for this problem often include a data-driven component that uses a long history of prior observations to model the behaviour of other agents (or agent types) and to determine the ad hoc agent's behaviour. In many practical domains, it is challenging to find large training datasets, and necessary to understand and incrementally extend the existing models to account for changes in team composition or domain attributes. Our architecture combines th…
This paper is concerned with evaluating different multiagent learning (MAL) algorithms in problems where individual agents may be heterogenous, in the sense of utilizing different learning strategies, without the opportunity for prior agreements or information regarding coordination. Such a situation arises in ad hoc team problems, a model of many practical multiagent systems applications. Prior work in multiagent learning has often been focussed on homogeneous groups of agents, meaning that all agents were identical and a priori aware of this fact. Also, those algorithms that are specifically…
Facebook AI Research（脸书人工智能研究院）；University of Oxford（牛津大学）
The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together. However, optimal SP policies commonly contain arbitrary conventions ("handshakes") and are not compatible with other, independently trained agents or humans. This latter desiderata was recently formalized by Hu et al. 2020 as the zero-shot coordination (ZSC) setting and partially addressed with their Other-Play (OP) algorithm, which showed improved ZSC and human-AI performance in the card game Hanabi. OP assumes access to the symmetries of th…
Cal Poly（加州州立理工大学）；NYU（纽约大学）；USC（南加州大学）；HRI Europe（本田欧洲研究所）
Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination. Evaluating an agent in this setting requires a diverse population of potential partners, but so far, the behavioral diversity of agents has not been considered in a systematic way. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse…
