The Role of Metareasoning in Achieving Effective Multiagent Coordination

Rubinstein, Zachary B.; Smith, Stephen F.; Zimmerman, Terry

doi:10.7551/mitpress/9780262014809.003.0014

Cited by 1 publication

(3 citation statements)

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“…1. In the studies that we reviewed, this structure was very common in both non-cooperative MAS [28] and cooperative MAS [1], [5], [6], [9], [17], [20], [24], [26], [29], [30], [35], [36]. Because the meta-levels are independent, no additional communication or coordination between the agents is required, so implementing this structure is easier than implementing a decentralized structure.…”

Section: A Independent Metareasoningmentioning

confidence: 99%

“…In the MAS considered by Rubinstein et al [30], an agent has multiple scheduling algorithms (one that modifies its own schedule and another that creates hypothetical schedules in This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: B Planning and Schedulingmentioning

confidence: 99%

“…Modifying a parameter can also affect the computational resources that reasoning algorithms consume. For example, Rubinstein et al [30] used a metareasoning rule to modify parameters that affected the search algorithms used by different reasoning algorithms that were competing for resources; modifying the parameter values affected the computation resources used and the quality of the solution obtained by these algorithms (such as modifying its own scheduling and creating hypothetical schedules in response to queries from other agents).…”

Section: B Modifying a Parameter Valuementioning

confidence: 99%

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

et al. 2020

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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.

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