Reputation-driven Decision-making in Networks of Stochastic Agents

• URL: http://arxiv.org/abs/2008.11791v2
• Date: Tue, 20 Oct 2020 07:57:32 GMT
• Title: Reputation-driven Decision-making in Networks of Stochastic Agents
• Abstract summary: We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domains in which agent reputation is a key driver of the interactions between agents. In a series of experiments, RepNet agents are shown to be able to adapt their own behavior to the past behavior and reliability of the remaining agents of the network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies multi-agent systems that involve networks of self-interested agents. We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domains in which agent reputation is a key driver of the interactions between agents. The fundamentals are based on the principles of RepNet-POMDP, a framework developed by Rens et al. in 2018, but addresses its mathematical inconsistencies and alleviates its intractability by only considering fully observable environments. We furthermore use an online learning algorithm for finding approximate solutions to RepNet-MDPs. In a series of experiments, RepNet agents are shown to be able to adapt their own behavior to the past behavior and reliability of the remaining agents of the network. Finally, our work identifies a limitation of the framework in its current formulation that prevents its agents from learning in circumstances in which they are not a primary actor.

Related papers

• The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development? [80.24951682268332]
  We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development.<n>To ensure evaluation integrity, this framework is secured by multi-layer defenses against reward hacking.<n>We demonstrate that meta-agents rarely match human-engineered baseline policies, and the few that do are dominated by proprietary frontier models.
  arXiv  Detail & Related papers  (2026-06-03T04:58:17Z)
• AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions [78.49000936275773]
  We introduce AgentHijack, a benchmark designed to evaluate the robustness of computer-use agents under common corruptions.<n>We evaluate a variety of desktop tasks that utilize MLLM-based agents and discover that even minor instances of corruption can result in substantial performance degradation.<n>We propose AgentHijack-Agent, a framework that integrates an action generator with enhanced grounding capabilities and an onlooker responsible for behavior summarization and environment checking.
  arXiv  Detail & Related papers  (2026-05-25T11:09:22Z)
• AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition [62.16431420189863]
  LLM-based multimodal emotion recognition relies on static parametric memory and often hallucinates when interpreting nuanced affective states.<n>We introduce AffectAgent, an affect-oriented multi-agent retrieval-augmented generation framework.<n>AffectAgent comprises three jointly optimized agents, namely a query planner, an evidence filter, and an emotion generator.
  arXiv  Detail & Related papers  (2026-04-14T13:49:19Z)
• NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration [0.669087470775851]
  We introduce NetAgentBench, a benchmark that evaluates agent interactions through a Finite State Machine (FSM) formalization.<n>This provides the networking landscape with a rigorous foundation to measure complex, multi-turn operational behaviors.
  arXiv  Detail & Related papers  (2026-04-03T05:11:05Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress [71.02263260394261]
  Large language models (LLMs) still encounter challenges in multi-turn decision-making tasks.<n>We build process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process.<n>AgentPRM captures both the interdependence between sequential decisions and their contribution to the final goal.
  arXiv  Detail & Related papers  (2025-11-11T14:57:54Z)
• AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs [8.912989700822127]
  We propose AgentsNet, a new benchmark for multi-agent reasoning.<n>We evaluate a variety of baseline methods on AgentsNet including homogeneous networks of agents.<n>We find that some frontier LLMs are already demonstrating strong performance for small networks but begin to fall off once the size of the network scales.
  arXiv  Detail & Related papers  (2025-07-11T14:13:22Z)
• WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback [74.82886755416949]
  We identify key reasoning skills essential for effective web agents.<n>We reconstruct the agent's reasoning algorithms into chain-of-thought rationales.<n>Our approach yields significant improvements across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T14:03:37Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems. In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy. Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models [50.19174067263255]
  We introduce prior preference learning techniques and self-revision schedules to help the agent excel in sparse-reward, continuous action, goal-based robotic control POMDP environments. We show that our agents offer improved performance over state-of-the-art models in terms of cumulative rewards, relative stability, and success rate.
  arXiv  Detail & Related papers  (2024-09-21T18:32:44Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes [7.464789724562025]
  This paper investigates continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs) In our study, we adopt a distributed multi-agent framework where individual agents have access only to their own rewards, lacking insights into the rewards of other agents.
  arXiv  Detail & Related papers  (2023-07-31T14:25:20Z)
• Policy Evaluation in Decentralized POMDPs with Belief Sharing [39.550233049869036]
  We consider a cooperative policy evaluation task in which agents are not assumed to observe the environment state directly. We propose a fully decentralized belief forming strategy that relies on individual updates and on localized interactions over a communication network.
  arXiv  Detail & Related papers  (2023-02-08T15:54:15Z)
• Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior [0.0]
  We integrate social' interactions into the MARL setup through a user-defined relational network. We examine the effects of agent-agent relations on the rise of emergent behaviors.
  arXiv  Detail & Related papers  (2022-07-12T23:27:42Z)
• Emergence of Theory of Mind Collaboration in Multiagent Systems [65.97255691640561]
  We propose an adaptive training algorithm to develop effective collaboration between agents with ToM. We evaluate our algorithms with two games, where our algorithm surpasses all previous decentralized execution algorithms without modeling ToM.
  arXiv  Detail & Related papers  (2021-09-30T23:28:00Z)
• What is Going on Inside Recurrent Meta Reinforcement Learning Agents? [63.58053355357644]
  Recurrent meta reinforcement learning (meta-RL) agents are agents that employ a recurrent neural network (RNN) for the purpose of "learning a learning algorithm" We shed light on the internal working mechanisms of these agents by reformulating the meta-RL problem using the Partially Observable Markov Decision Process (POMDP) framework.
  arXiv  Detail & Related papers  (2021-04-29T20:34:39Z)
• Multi-Agent Decentralized Belief Propagation on Graphs [0.0]
  We consider the problem of interactive partially observable Markov decision processes (I-POMDPs) We propose a decentralized belief propagation algorithm for the problem. Our work appears to be the first study of decentralized belief propagation algorithm for networked multi-agent I-POMDPs.
  arXiv  Detail & Related papers  (2020-11-06T18:16:26Z)
• Maximizing Information Gain in Partially Observable Environments via Prediction Reward [64.24528565312463]
  This paper tackles the challenge of using belief-based rewards for a deep RL agent. We derive the exact error between negative entropy and the expected prediction reward. This insight provides theoretical motivation for several fields using prediction rewards.
  arXiv  Detail & Related papers  (2020-05-11T08:13:49Z)

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.