摘要
群体智能是生物群体通过个体间交互而涌现的启发人工智能认知方法设计的新型计算范式,为解决复杂问题提供了独特途径。对生物群体智能启发的优化认知方法与博弈认知方法进行梳理。综述生物群体智能在集中式、分布式和网络化3种组织范式启发下的优化认知方法具体实现机制,揭示群体智能优化算法如何平衡探索与开发以高效求解问题。从个体策略的适应与优化以及群体交互的互动与演化两个核心维度,分析生物群体智能启发的博弈认知理论内涵与关键动态过程。通过分析生物群体智能启发的认知模式,有望为设计更高效、更鲁棒的人工智能算法提供理论参考与启示。
Swarm intelligence emerges from interactions among individuals within biological populations,serving as a novel computational paradigm that inspires the design of artificial intelligence cognitive methods.It offers unique approaches for solving complex problems.This paper systematically reviews optimization cognition methods and game cognition methods inspired by biological swarm intelligence.First,this paper summarizes the specific implementation mechanisms of optimization cognition methods under three organizational paradigms inspired by biological swarm intelligence:centralized,distributed,and networked,revealing how swarm intelligence optimization algorithms balance exploration and exploitation to solve problems efficiently.Second,this paper analyzes the theoretical underpinnings and key dynamic processes of game cognition methods inspired by biological swarm intelligence from two core dimensions:the adaptation and optimization of individual strategies,and the interaction and evolution of swarm interaction behaviors.By examining these cognitive models derived from biological swarm intelligence,this paper aims to provide theoretical references and design insights for developing more efficient and robust artificial intelligence algorithms.
作者
詹志辉
石清远
ZHAN Zhihui;SHI Qingyuan(College of Artificial Intelligence,Nankai University Tianjin 300350)
出处
《中国基础科学》
2025年第5期1-9,共9页
China Basic Science
基金
国家重点研发计划青年科学家项目(2024YFF0509600)
国家自然科学基金项目(62576175)。
关键词
生物群体智能
优化算法
博弈算法
最优化问题
博弈论
biological swarm intelligence
optimization algorithms
game algorithms
optimization problems
game theory
