mlsanityData to 7 Oct 2026

Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms

arXiv 2502.04388 ↗First posted 5 Feb 2025cs.MA cs.AIcited by 0

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Abstract (arXiv)

Artificial Intelligence (AI) agents capable of autonomous learning and independent decision-making hold great promise for addressing complex challenges across various critical infrastructure domains, including transportation, energy systems, and manufacturing. However, the surge in the design and deployment of AI systems, driven by various stakeholders with distinct and unaligned objectives, introduces a crucial challenge: How can uncoordinated AI systems coexist and evolve harmoniously in shared environments without creating chaos or compromising safety? To address this, we advocate for a fundamental rethinking of existing multi-agent frameworks, such as multi-agent systems and game theory, which are largely limited to predefined rules and static objective structures. We posit that AI agents should be empowered to adjust their objectives dynamically, make compromises, form coalitions, and safely compete or cooperate through evolving relationships and social feedback. Through two case studies in critical infrastructure applications, we call for a shift toward the emergent, self-organizing, and context-aware nature of these multi-agentic AI systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 5 Feb 2025.

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24 of 47 references.

References · 24

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
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12 more
  1. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  2. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  3. 2019Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learningcited by 53
  4. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  5. 2019Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Drivingcited by 36
  6. 2023ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agentscited by 33
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  11. 2024Exploring Equilibrium Strategies in Network Games with Generative AIcited by 3
  12. 2023Responsible Emergent Multi-Agent Behaviorcited by 1

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