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Autonomous multiagent systems can be used in different domains such as agriculture, search and rescue, and fire protection because they can accomplish large missions more quickly and robustly by dividing them into separate tasks. Using multiple agents introduces additional complexity, which makes autonomous reasoning and decision making more challenging, however. Because agents such as ground robots, unmanned air vehicles, and autonomous underwater vehicles may have limited computational resources, they may need computationally efficient yet powerful reasoning algorithms (decision-making processes that perform deliberation and means-end reasoning). Metareasoning, which is reasoning about these reasoning algorithms, offers a way to tackle these challenges by monitoring and controlling reasoning algorithms to improve agent and system performance. Although metareasoning approaches for individual computational agents have been studied, no survey of metareasoning in multiagent systems (MAS) has yet appeared. This survey fills the existing gap by discussing the multiagent metareasoning approaches that have been studied in the literature. It identifies metareasoning structures, applications of metareasoning to reasoning problems, and the modes (techniques) used to control reasoning processes. This survey contributes to the study of MAS by providing a framework for discussing multiagent metareasoning, highlighting successful approaches, and indicating areas where future work may be fruitful.

Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey