提出具身集体智能框架，助力AI系统自适应复杂环境。
Conceptual Framework Toward Embodied Collective Adaptive Intelligence
- 构建具身集体智能的通用设计框架
- 明确任务泛化、韧性、可扩展性等核心属性
- 适合研究自组织AI系统的学者与工程师
集体适应智能（CAI）代表了一种变革性的具身人工智能方法，即众多自主智能体协同工作、自我适应并自组织以应对复杂动态环境。通过使系统能够响应未预见挑战而重新配置，CAI在真实场景中实现稳健表现。本文提出一个用于设计和分析CAI的概念框架，明确了任务泛化、韧性、可扩展性和自组装等关键属性，旨在连接理论基础与工程实践，为开发更具韧性、可扩展性和适应性的智能系统提供结构化指导。
原文摘要 · Abstract (English)
Collective Adaptive Intelligence (CAI) represent a transformative approach in embodied AI, wherein numerous autonomous agents collaborate, adapt, and self-organize to navigate complex, dynamic environments. By enabling systems to reconfigure themselves in response to unforeseen challenges, CAI facilitate robust performance in real-world scenarios. This article introduces a conceptual framework for designing and analyzing CAI. It delineates key attributes including task generalization, resilience, scalability, and self-assembly, aiming to bridge theoretical foundations with practical methodologies for engineering adaptive, emergent intelligence. By providing a structured foundation for understanding and implementing CAI, this work seeks to guide researchers and practitioners in developing more resilient, scalable, and adaptable AI systems across various domains.
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