GMAI
Generalized Multi-Agent Intelligence (GMAI) is a research paradigm in artificial intelligence that studies systems composed of multiple autonomous agents capable of learning, adapting, and coordinating across a broad range of tasks and environments. GMAI seeks to develop agents whose policies generalize beyond a single task and whose interactions yield robust collective behavior.
In a GMAI system, agents operate with local observations and actions within a shared or partially shared
Key challenges include non-stationarity due to changing policies, credit assignment across agents, scalable coordination in large
Applications span robotics fleets, autonomous traffic and drone swarms, distributed sensing and monitoring, smart grids, logistics,
GMAI is related to, but distinct from, artificial general intelligence (AGI) and traditional multi-agent reinforcement learning.