Multi-Agent Systems for Bullying Intervention

Bullying is a pervasive problem in educational settings, leading to serious emotional and psychological harm for those involved. It is a complex social phenomenon involving multiple roles, including bullies, victims, and observers, each contributing to the dynamics of bullying scenarios. In this paper, we propose a novel Multi-Agent system (MAS) framework aimed at detecting and intervening in bullying cases through the integration of Theory of Mind (ToM), Reinforcement Learning (RL), and Continual Learning (CL). Our approach leverages ToM to allow agents to infer the mental states of others, enabling context-aware decision-making for effective intervention strategies. RL is used to allow the observer agent to learn from past interactions, improving its ability to recognize bullying behaviors and refine its responses. CL ensures the system can adapt to new behaviors and evolving environments, maintaining its effectiveness over time. We present abstraction mechanisms based on Theory-Theory and Simulation Theory, which allow the system to reason about complex social interactions either through predefined rules or simulations. This paper outlines the theoretical framework and design of the proposed algorithm, offering a responsive, flexible, adaptive, and capable solution for bullying prevention and intervention in educational contexts, where socially intelligent systems can play a key role in creating safer environments.