AgentAI: A comprehensive survey on autonomous agents in distributed AI for industry 4.0
AgentAI: Всеобъемлющее обзорное исследование автономных агентов в распределённом ИИ для Индустрии 4.0
2025-06-02
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AgentAIIndustry 4.0autonomous agentsdistributed Artificial Intelligencemulti-domain taxonomy
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Abstract (AI)
AgentAI represents a transformative approach within distributed Artificial Intelligence (AI) in which autonomous agents work either individually or collaboratively in decentralized environments to address challenging problems. AgentAI enhances scalability, robustness, and flexibility by utilizing advanced communication, learning, and decision-making capabilities, making it integral to diverse applications in Industry 4.0. The ability of AI systems to interpret sensory data in open-world environments has seen significant advancements in recent years. This progress emphasizes the need to move beyond reductionist approaches and embrace more embodied and cohesive systems, which integrate foundational models into agent-driven actions. Existing surveys often focus on isolated domains or specific autonomy levels, lacking a cohesive analysis that spans the full spectrum of AgentAI development in Industry 4.0. This survey explicitly fills this gap by introducing a multi-domain taxonomy and by systematically analyzing both non-autonomous and fully autonomous AgentAI systems, offering a comprehensive synthesis not previously available in the literature. Additionally, the paper extends the discussion to Industry 5.0 and 6.0, exploring the evolution of AgentAI from automation to collaboration and, ultimately, to fully autonomous systems. This comprehensive analysis highlights the potential of AgentAI in driving industries toward a more efficient, sustainable, and adaptable future.
Key Findings
1
Argues that recent advances in sensory interpretation and foundational models motivate a shift from reductionist to more embodied, cohesive agent-driven systems.
2
Extends discussion to Industry 5.0 and 6.0, outlining AgentAI's evolution from automation to collaboration and toward fully autonomous industrial systems.
3
Identifies state-of-the-art techniques and key challenges in AgentAI systems, including communication, learning, and decision-making in decentralized environments.
4
Introduces a comprehensive multi-domain taxonomy of AgentAI applications specific to Industry 4.0, filling a gap in existing literature.
5
Provides a systematic analysis covering both non-autonomous and fully autonomous AgentAI systems across the full spectrum of AgentAI development.
Research Object
Autonomous agents (AgentAI) deployed in distributed AI systems for Industry 4.0
Research Subject
Their applications, taxonomy, techniques, challenges, and the spectrum of autonomy (from non-autonomous to fully autonomous) including communication, learning, decision-making, and integration of foundational models within Industry 4.0 contexts
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2025-06-02
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