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A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality

Shan Suthaharan

TL;DR

This work addresses decentralized decision-making in AI by proposing a nature-inspired colony-of-AI system that uses fast, detailed, and organized learner roles mapped to CNN backbones. It introduces intra- and inter-marriage via Genetic Algorithms and triplet-based knowledge sharing to evolve diverse child agents within both multi-model and mixture-model colonies. Empirical results on constrained MNIST show high predictive performance (F1-scores between 82% and 95%) and robust ROC behavior, with quantitative diversity-quality metrics supporting the efficacy of the approach. The proposed framework advances multi-agent AI by enabling scalable diversification, collective decision-making, and potential explainability, with future work focusing on more complex datasets and comparisons to traditional multi-agent systems.

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.

A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality

TL;DR

This work addresses decentralized decision-making in AI by proposing a nature-inspired colony-of-AI system that uses fast, detailed, and organized learner roles mapped to CNN backbones. It introduces intra- and inter-marriage via Genetic Algorithms and triplet-based knowledge sharing to evolve diverse child agents within both multi-model and mixture-model colonies. Empirical results on constrained MNIST show high predictive performance (F1-scores between 82% and 95%) and robust ROC behavior, with quantitative diversity-quality metrics supporting the efficacy of the approach. The proposed framework advances multi-agent AI by enabling scalable diversification, collective decision-making, and potential explainability, with future work focusing on more complex datasets and comparisons to traditional multi-agent systems.

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.

Paper Structure

This paper contains 25 sections, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Multi-model colony of AI
  4. Fast learners
  5. Detailed learners
  6. Organized learners
  7. Intra-marriage of AI
  8. Mixture-model colony of AI
  9. Fast learner and Detailed learner
  10. Fast learner and Organized learner
  11. Detailed learner and Organized learner
  12. Diversity-Quality trade-off estimator
  13. Results
  14. Results from multi-model colony of AI
  15. Intra-marriage VGG16 AI agents.
  16. ...and 10 more sections

Figures (8)

  • Figure 1: Overview of the concept of colony of AI.
  • Figure 2: Fast learner: VGG16 architecture and the understanding of knowledge sharing by AI agents. Note: dim-reduc represents dimensionality reduction, rel-feature represents relevant features, and inv-prop represents invariant properties.
  • Figure 3: Pre-trained VGG16 internal architecture with knowledge mapping.
  • Figure 4: Pre-trained VGG19 internal architecture with knowledge mapping.
  • Figure 5: Pre-trained ResNet50 internal architecture with knowledge mapping.
  • ...and 3 more figures