Abstract
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.
Highlights
Metareasoning describes reasoning about one’s own decision-making process [15]
This review considers communication as an object-level process because some reasoning algorithms must communicate with other agents as part of their decision-making process
This paper aims to address that gap by describing key aspects of previous research on metareasoning in multiagent systems, where there are challenges and opportunities for metareasoning that do not exist when a single agent is VOLUME 8, 2020 operating independently
Summary
Metareasoning describes reasoning about one’s own decision-making process [15]. In dynamic, uncertain environments, metareasoning—a type of self-adaptation—seeks to improve an autonomous agent’s performance by monitoring and controlling the agent’s reasoning and decision-making processes. In a MAS, an agent’s object-level may include different types of reasoning algorithms such as communication, coordination, and team formation (which are not relevant when there is only one agent). This paper aims to address that gap by describing key aspects of previous research on metareasoning in multiagent systems, where there are challenges and opportunities for metareasoning that do not exist when a single agent is VOLUME 8, 2020 operating independently. This paper describes previous work on multiagent metareasoning from three perspectives: (1) the metareasoning structure, the relationship (if any) between the agents at the meta-level; (2) the metareasoning problem, the particular aspect of the agent’s reasoning that the meta-level is controlling; and (3) the metareasoning mode, the means by which the meta-level modifies object-level reasoning.
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