Distributed articial intelligence is the subfield of articial intelligence that focuses on complex systems that are inhabited by multiple agents. The main goal of research in this area is to provide principles for the construction and application of multi-agent systems as well as means for coordinating the behavior of multiple independent agents.
There is a variety of application fields for multi-agent systems and, what is of special interest, multi-agent reinforcement learning (MARL) systems. Reinforcement learning and related approaches have been applied to optimize agent behavior in the scope of mobile telephony (e.g. for channel allocation or ad-hoc networks), network technology (e.g. for data packet routing), energy and oil distribution (e.g. for electric power generation networks or for optimizing pipeline operations), computing power management (e.g. for load balancing across servers, for distributed computing or for constrained job dispatching in mainframe systems), autonomous robots (e.g. for robotic soccer or for exploration of unknown territory) and computer games (e.g. for first-person shooter games), rescue operations (e.g. for enabling units to decide independently which sites to search and secure), surveillance and security tasks (e.g. for distributed sensor networks, for patrolling task, or for military application), and for the field of production planning and factory optimization, where machines may act independently with the goal of achieving maximal joint productivity.