A new survey paper details advancements in multi-agent cooperative decision-making, a field crucial for AI systems in complex tasks like autonomous driving and disaster rescue. The paper categorizes current approaches into five types: rule-based (including fuzzy logic), game theory-based, evolutionary algorithms-based, deep multi-agent reinforcement learning (MARL)-based, and large language models (LLMs) reasoning-based. It highlights MARL and LLM-based methods for their significant advantages and discusses future research directions and challenges. AI
IMPACT Provides a comprehensive overview of techniques for AI systems to collaborate on complex tasks.
RANK_REASON The cluster contains a survey paper published on arXiv detailing research in multi-agent systems. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Deep Multi-Agent Reinforcement Learning:A Survey
- Evolutionary Algorithms
- fuzzy logic
- game theory
- large-language models
- Multi-agent reinforcement learning
- multi-agent system
- Weiqiang Jin
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