本文提出了一种基于卷积神经网络（CNN）的AI智能体群构建方法，旨在模仿生物系统（如蚁群或人类群体）的环境，以单个系统形式执行多项任务，例如预测或分类。该系统包含快速学习者、详细学习者和组织学习者，这些智能体通过局部学习和集体决策来提升性能。通过引入遗传算法及其交叉和变异机制，增强了AI智能体群的多样性和质量。此外，该方法将快速、详细和组织学习者与预训练的VGG16、VGG19和ResNet50模型进行一对一映射，并通过AI的“内部”和“跨越”结合过程，使智能体共享知识，生成多样化的子智能体以执行新任务。模拟结果表明，该AI智能体群在预测性能方面表现出色，F1分数在82%到95%之间。

🐜该研究构建了一个基于CNN的AI智能体群，模拟生物群体的运作方式，以执行预测或分类等多任务。这种设计模仿了如蚁群或人类群体等自然环境中的协作和分工模式。

🧠 该系统包含三种类型的学习者：快速学习者、详细学习者和组织学习者。这些学习者通过本地学习和集体决策来提高整体性能，从而实现更高效的任务完成。

🧬 通过遗传算法，该系统引入了交叉和变异机制，增强了AI智能体群的多样性和质量。这种方法促进了智能体群体的进化和适应能力。

🤝 系统将不同类型的学习者与预训练的VGG16、VGG19和ResNet50模型进行映射，使得智能体能够共享知识，生成多样化的子智能体。这种知识共享机制提高了智能体的整体表现。

📊 模拟结果显示，该AI智能体群在预测任务中表现出色，F1分数达到82%到95%。这表明该方法能够构建一个高效、多样化的AI系统，能够做出高质量的集体决策。

arXiv:2504.05365v1 Announce Type: cross Abstract: 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.