一种受自然启发的具有快速、细致且有组织学习者智能体的AI系统群体，用于增强多样性与质量
A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality
AI总结：
本文提出一种受生物群体启发的基于CNN的多智能体AI群体系统，通过将快速、细致和有组织学习者分别映射到VGG16、VGG19和ResNet50，并结合遗传算法交叉变异及AI联姻机制生成多样化子代智能体，从而在单一系统中提升集体决策的多样性与质量。
摘要：
卷积神经网络（CNNs）与多智能体系统的概念是人工智能（AI）中两个重要的研究领域。本文提出一种方法，构建一个基于CNN的AI智能体群体，作为单一系统在环境中执行多项任务（例如预测或分类）。所提出的系统模仿生物系统的自然环境，如蚁群或人类群体。所提出的AI群体被定义为一个基于角色的系统，通过纳入快速学习者、细致学习者和有组织学习者等AI智能体，独特地有助于在环境中完成任务。这些学习者能够增强其局部学习能力，并作为AI智能体群体的单一系统增强其集体决策。该方法还借助遗传算法及其交叉和变异机制，增强了AI群体的多样性和质量。通过在快速、细致和有组织学习者与预训练的VGG16、VGG19和ResNet50模型之间分别引入唯一的一对一映射，实现了AI群体中这些学习者的进化。这种基于角色的方法通过称为AI内部联姻和AI间联姻的过程，利用AI模型创建两个父代AI智能体，使它们能够基于概率规则共享其学习到的知识（权重和偏置），并产生多样化的子代AI智能体以执行新任务。这一过程将形成一个由多模型和混合模型AI智能体家族组成的AI群体，以提高多样性和质量。仿真结果表明，使用VGG16、VGG19和ResNet50模型构建的AI群体能够提供一个单一系统，生成具有出色预测性能的子代AI智能体，其F1分数介于82%至95%之间，从而在任务上做出多样化的集体和高质量决策。
英文摘要：
The concepts of convolutional neural networks (CNNs) and multi-agent systems are two important areas of research in artificial intelligence (AI). In this paper, we present an approach that builds a CNN-based colony of AI agents to serve as a single system and perform multiple tasks (e.g., predictions or classifications) in an environment. The proposed system impersonates the natural environment of a biological system, like an ant colony or a human colony. The proposed colony of AI that is defined as a role-based system uniquely contributes to accomplish tasks in an environment by incorporating AI agents that are fast learners, detailed learners, and organized learners. These learners can enhance their localized learning and their collective decisions as a single system of colony of AI agents. This approach also enhances the diversity and quality of the colony of AI with the help of Genetic Algorithms and their crossover and mutation mechanisms. The evolution of fast, detailed, and organized learners in the colony of AI is achieved by introducing a unique one-to-one mapping between these learners and the pretrained VGG16, VGG19, and ResNet50 models, respectively. This role-based approach creates two parent-AI agents using the AI models through the processes, called the intra- and inter-marriage of AI, so that they can share their learned knowledge (weights and biases) based on a probabilistic rule and produce diversified child-AI agents to perform new tasks. This process will form a colony of AI that consists of families of multi-model and mixture-model AI agents to improve diversity and quality. Simulations show that the colony of AI, built using the VGG16, VGG19, and ResNet50 models, can provide a single system that generates child-AI agents of excellent predictive performance, ranging between 82% and 95% of F1-scores, to make diversified collective and quality decisions on a task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
