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 environment. Communication protocols, distributed planning, and cooperation strategies enable coordination, resource allocation, and conflict resolution. Methods include multi-agent reinforcement learning, meta-learning, transfer learning, and centralized training with decentralized execution.

Key challenges include non-stationarity due to changing policies, credit assignment across agents, scalable coordination in large teams, exploration-exploitation in distributed settings, and safety and alignment concerns when agents operate in real-world contexts. Additional concerns involve robustness to partial observability, communication constraints, and resilience to agent failures or adversarial behavior.

Applications span robotics fleets, autonomous traffic and drone swarms, distributed sensing and monitoring, smart grids, logistics, and disaster response. Research aims to achieve generalization across tasks, robustness in dynamic environments, and efficient collaboration under limited communication.

GMAI is related to, but distinct from, artificial general intelligence (AGI) and traditional multi-agent reinforcement learning. It focuses on cooperative, generalizable learning across multiple agents rather than single-agent general problem solving. The acronym also appears in other contexts outside AI, so clarification is often required when encountering the term.