Collaborative Problem

Collaborative problem-solving (CPS) research focuses on enabling multiple agents, whether robots, software programs, or humans, to work together effectively towards a shared goal. Current research emphasizes developing algorithms and architectures, such as multi-agent reinforcement learning (MARL) with techniques like mutual information maximization and game-theoretic approaches, to facilitate efficient coordination and information sharing among agents, even in decentralized settings. This field is significant for advancing artificial intelligence, particularly in robotics and distributed systems, and for providing insights into human collaboration dynamics through the lens of complex adaptive systems.