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<p>Effective swarm management requires detecting individuals that negatively impact overall performance. This paper proposes a method using Explainable AI (XAI) to both detect swarm-level anomalies and identify the causative agents. We train a convolutional neural network (CNN)-based neural network to classify swarms as normal or abnormal, then use Grad-CAM (an XAI technique) to pinpoint the responsible individuals. We simulate swarms using the Boid model, introducing “anomaly agents” with slightly altered parameters for Alignment, Cohesion, and Separation. A novel model, adding a Lambda Layer to VGG16, is proposed and compared with standard CNNs (VGG16, ResNet50, DenseNet121, EfficientNetB0). The Lambda Layer model achieved the highest accuracy in both anomaly detection and agent identification. Experimental results show high accuracy in identifying agents with altered Alignment and Separation parameters. However, identifying agents with altered Cohesion is more challenging due to their proximity to normal agents, leading to increased misidentifications. The results demonstrate the effectiveness of combining CNNs and XAI for anomaly detection and root cause analysis in swarms.</p>

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Detection of anomalous individuals in swarm using explainable AI

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Hiroshi Sato,
  • Yohei Fukuyama,
  • Masao Kubo,
  • Haruta Isobe,
  • Tomoyashu Deguchi

摘要

Effective swarm management requires detecting individuals that negatively impact overall performance. This paper proposes a method using Explainable AI (XAI) to both detect swarm-level anomalies and identify the causative agents. We train a convolutional neural network (CNN)-based neural network to classify swarms as normal or abnormal, then use Grad-CAM (an XAI technique) to pinpoint the responsible individuals. We simulate swarms using the Boid model, introducing “anomaly agents” with slightly altered parameters for Alignment, Cohesion, and Separation. A novel model, adding a Lambda Layer to VGG16, is proposed and compared with standard CNNs (VGG16, ResNet50, DenseNet121, EfficientNetB0). The Lambda Layer model achieved the highest accuracy in both anomaly detection and agent identification. Experimental results show high accuracy in identifying agents with altered Alignment and Separation parameters. However, identifying agents with altered Cohesion is more challenging due to their proximity to normal agents, leading to increased misidentifications. The results demonstrate the effectiveness of combining CNNs and XAI for anomaly detection and root cause analysis in swarms.