Multi-agent systems comprise multiple decision-making agents which act and interact autonomously in a shared environment towards achieving specified tasks, such as ISR missions. Each agent is a software system with interfaces to sense potentially noisy and incomplete information from the environment, and to choose actions within the environment such as high-level planning decisions (resource allocation and coalition formation) or low-level controls (movements of mobile assets). This theme seeks to develop new algorithms for agent decision-making in complex, dynamic multi-agent systems to enable efficient and scalable coordination of agents.A strong focus will be on data-driven methods based on deep reinforcement learning (RL) and multi-agent RL, whereby agents learn coordinated decision policies by autonomously exploring a complex action space and identifying optimal coordination strategies.Other important method components will include dynamic team formation in “ad hoc” scenarios where agents must collaborate with new, previously unknown agents on-the-fly. Theme leadsProfessor Mohan SridharanProf Timothy HospedalesCo-InvestigatorProf Ram Ramamoorthy This article was published on 2025-10-31
