Decision Making in Multiagent Systems: A Survey

TLDR

Multiagent systems such as intelligent transport, electric grids, and sensor networks rely on cooperative decision making to accomplish increasingly complex tasks. This survey examines recent advances in cooperative MAS decision‑making models, including Markov decision processes, game theory, swarm intelligence, and graph‑theoretic approaches. The authors review algorithms yielding optimal and suboptimal policies—such as reinforcement learning, dynamic programming, evolutionary computing, and neural networks—applied to robotics, wireless sensor networks, cognitive radio, transport systems, and smart grids, while outlining key terms and future challenges like big‑data integration, scalability, and standardized evaluation.

Abstract

Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this survey, we investigate state-of-the-art work within the past five years on cooperative MAS decision making models, including Markov decision processes, game theory, swarm intelligence, and graph theoretic models. We survey algorithms that result in optimal and suboptimal policies such as reinforcement learning, dynamic programming, evolutionary computing, and neural networks. We also discuss the application of these models to robotics, wireless sensor networks, cognitive radio networks, intelligent transport systems, and smart electric grids. In addition, we define key terms in the area and discuss remaining challenges that include incorporating big data advancements to decision making, developing autonomous, scalable and computationally efficient algorithms, tackling more complex tasks, and developing standardized evaluation metrics. While recent surveys have been published on this topic, we present a broader discussion of related models and applications.