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<p>Bullying is a complex social issue that requires adaptive and context-aware intervention strategies. This paper presents a multi-agent model that integrates Theory of Mind (ToM), Reinforcement Learning (RL), and Continual Learning (CL) to improve the detection and intervention of bullying behaviors. ToM allows agents to infer the beliefs, intentions, and emotions of others, enabling socially aware decision-making. RL equips the observer agent with the ability to learn from past experiences, refining its intervention strategies dynamically. CL ensures long-term adaptability by allowing the system to retain learned behaviors while continuously adjusting to new bullying patterns. The proposed approach incorporates abstraction mechanisms derived from Theory-Theory (TT) and Simulation Theory (ST), enabling agents to reason about bullying interactions either through predefined rules or simulated experiences. The system has been evaluated in a simulated school environment with varying levels of bullying severity, demonstrating its effectiveness in dynamically adapting intervention strategies. The results indicate that combining ToM, RL, and CL leads to superior performance compared to standard RL-based approaches, particularly in high-risk bullying scenarios. This work provides a foundation for the development of socially intelligent AI systems capable of proactive and context-sensitive intervention in educational settings.</p>

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Theory of mind and continual reinforcement learning for bullying intervention

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月14日

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  • Luis Zhinin-Vera,
  • José J. González-García,
  • Víctor López-Jaquero,
  • Elena Navarro,
  • Pascual González

摘要

Bullying is a complex social issue that requires adaptive and context-aware intervention strategies. This paper presents a multi-agent model that integrates Theory of Mind (ToM), Reinforcement Learning (RL), and Continual Learning (CL) to improve the detection and intervention of bullying behaviors. ToM allows agents to infer the beliefs, intentions, and emotions of others, enabling socially aware decision-making. RL equips the observer agent with the ability to learn from past experiences, refining its intervention strategies dynamically. CL ensures long-term adaptability by allowing the system to retain learned behaviors while continuously adjusting to new bullying patterns. The proposed approach incorporates abstraction mechanisms derived from Theory-Theory (TT) and Simulation Theory (ST), enabling agents to reason about bullying interactions either through predefined rules or simulated experiences. The system has been evaluated in a simulated school environment with varying levels of bullying severity, demonstrating its effectiveness in dynamically adapting intervention strategies. The results indicate that combining ToM, RL, and CL leads to superior performance compared to standard RL-based approaches, particularly in high-risk bullying scenarios. This work provides a foundation for the development of socially intelligent AI systems capable of proactive and context-sensitive intervention in educational settings.