Preventing Rogue Agents Improves Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2502.05986v1
• Date: Sun, 09 Feb 2025 18:35:08 GMT
• Title: Preventing Rogue Agents Improves Multi-Agent Collaboration
• Authors: Ohav Barbi, Ori Yoran, Mor Geva,
• Abstract summary: Multi-agent systems, where specialized agents collaborate to solve a shared task hold great potential. A single agent can cause the entire system to fail. In this work, we propose to $textitmonitor$ agents during action prediction and $textitintervene$ when a future error is likely to occur.
• Score: 21.955058255432974
• License:
• Abstract: Multi-agent systems, where specialized agents collaborate to solve a shared task hold great potential, from increased modularity to simulating complex environments. However, they also have a major caveat -- a single agent can cause the entire system to fail. Consider a simple game where the knowledge to solve the task is distributed between agents, which share information in a communication channel. At each round, any of the agents can terminate the game and make the final prediction, even if they are uncertain about the outcome of their action. Detection of such rogue agents $\textit{before they act}$ may prevent the system's failure. In this work, we propose to $\textit{monitor}$ agents during action prediction and $\textit{intervene}$ when a future error is likely to occur. To test our approach, we introduce WhoDunitEnv, a multi-agent collaboration environment that allows modular control over task complexity and communication structure. Experiments on two variants of WhoDunitEnv and the GovSim environment for resource sustainability show that our approach leads to substantial performance gains up to 17.4% and 20%, respectively. Moreover, a thorough analysis shows that our monitors successfully identify critical points of agent confusion and our interventions effectively stop agent errors from propagating.

Related papers

• Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions [6.234292942334148]
  We propose a multi-agent cooperative learning algorithm that is robust to adversarial corruptions. As a side-product, our algorithm also improves the state-of-the-art regret bounds when reducing to both the single-agent and homogeneous multi-agent scenarios.
  arXiv  Detail & Related papers  (2024-11-12T20:20:26Z)
• On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents [58.79302663733703]
  Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents. However, the impact of clumsy or even malicious agents, on the overall performance of the system remains underexplored. This paper investigates what is the resilience of various system structures under faulty agents.
  arXiv  Detail & Related papers  (2024-08-02T03:25:20Z)
• Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
  We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena. We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search. We also use ARE to rigorously evaluate how the robustness changes as new components are added.
  arXiv  Detail & Related papers  (2024-06-18T17:32:48Z)
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments [116.97648507802926]
  Large language models (LLMs) are considered a promising foundation to build such agents. We take the first step towards building generally-capable LLM-based agents with self-evolution ability. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration.
  arXiv  Detail & Related papers  (2024-06-06T15:15:41Z)
• Malicious Agent Detection for Robust Multi-Agent Collaborative Perception [52.261231738242266]
  Multi-agent collaborative (MAC) perception is more vulnerable to adversarial attacks than single-agent perception. We propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X.
  arXiv  Detail & Related papers  (2023-10-18T11:36:42Z)
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [93.38830440346783]
  We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
  arXiv  Detail & Related papers  (2023-08-21T16:47:11Z)
• Decentralized scheduling through an adaptive, trading-based multi-agent system [1.7403133838762448]
  In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. This work applies a trading approach to a simulated scheduling environment, where the agents are responsible for the assignment of incoming jobs to compute cores. The agents can trade the usage right of computational cores to process high-priority, high-reward jobs faster than low-priority, low-reward jobs.
  arXiv  Detail & Related papers  (2022-07-05T13:50:18Z)
• Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs [65.23158435596518]
  Solving the multi-vehicle routing problem as a team Markov game with partially observable costs. Our multi-agent reinforcement learning approach, the so-called multi-agent Neural Rewriter, builds on the single-agent Neural Rewriter to solve the problem by iteratively rewriting solutions.
  arXiv  Detail & Related papers  (2022-06-13T09:17:40Z)
• Ubiquitous Distributed Deep Reinforcement Learning at the Edge: Analyzing Byzantine Agents in Discrete Action Spaces [0.06554326244334865]
  This paper discusses some of the challenges in multi-agent distributed deep reinforcement learning that can occur in the presence of byzantine or malfunctioning agents. We show how wrong discrete actions can significantly affect the collaborative learning effort. Experiments are carried out in a simulation environment using the Atari testbed for the discrete action spaces, and advantage actor-critic (A2C) for the distributed multi-agent training.
  arXiv  Detail & Related papers  (2020-08-18T11:25:39Z)
• Robust Multi-Agent Multi-Armed Bandits [26.26185074977412]
  Recent works have shown that agents facing independent instances of a $K$-armed bandit can collaborate to decrease regret. We show that collaboration indeed decreases regret for this algorithm, assuming $m$ is small compared to $K$ but without assumptions on malicious agents' behavior.
  arXiv  Detail & Related papers  (2020-07-07T22:27:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.