Cooperative Decision-Making in Decentralized Multiple-Robot Systems: The Best-of-N Problem

Parker, Chris A. C.; Zhang, Hong

doi:10.1109/tmech.2009.2014370

Cited by 79 publications

(66 citation statements)

References 24 publications

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“…This feature is particularly relevant to guide the choices of designers during the design of a collective decisionmaking strategy. When the option quality is static, designers favor collective decision-making strategies that results in consensus decisions (Parker and Zhang, 2009;Montes de Oca et al, 2011;Scheidler et al, 2016). Differently, when the option quality is dynamic, i.e., a function of time, designers favor strategies that result in a large majority of robots in the swarm favoring the same option without converging to consensus (Parker and Zhang, 2010;Arvin et al, 2014).…”

Section: The Best-of-n Problemmentioning

confidence: 99%

The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

2017

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The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making problem dealt with by the swarm. We find that, so far, the literature has primarily focused on certain variants of the best-of-n problem, while other variants have been the subject of only a few isolated studies. Additionally, we consider a second taxonomy about the design methodologies used to develop collective decision-making strategies. Based on this second taxonomy, we provide an in-depth survey of the literature that details the strategies proposed so far and discusses the advantages and disadvantages of current design methodologies.

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Section: The Best-of-n Problemmentioning

confidence: 99%

The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

2017

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“…Recently, Parker and Zhang tackled the problem of selecting the best of a set of alternatives with robots [23]. In their work, robots need to know whether there is a sufficient number of robots in favor of one alternative before committing to it.…”

Section: Related Workmentioning

confidence: 99%

Incremental Social Learning Applied to a Decentralized Decision-Making Mechanism: Collective Learning Made Faster

Stuetzle

Birattari

et al. 2010

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems

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Positive feedback and a consensus-building procedure are the key elements of a self-organized decision-making mechanism that allows a population of agents to collectively determine which of two actions is the fastest to execute. Such a mechanism can be seen as a collective learning algorithm because even though individual agents do not directly compare the available alternatives, the population is able to select the action that takes less time to perform, thus potentially improving the efficiency of the system. However, when a large population is involved, the time required to reach consensus on one of the available choices may render impractical such a decision-making mechanism.In this paper, we tackle this problem by applying the incremental social learning approach, which consists of a growing population size coupled with a social learning mechanism. The obtained experimental results show that by using the incremental social learning approach, the collective learning process can be accelerated substantially. The conditions under which this is true are described.

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“…For example, the best-of-n problem [16] requires a multi-agent swarm to select the best option from n mutually exclusive alternatives, based only on localised feedback and learning. In fact, this generic problem underpins a wide variety of distributed decision-making tasks (see [23] for an overview).…”

Section: Introductionmentioning

confidence: 99%

Robust distributed decision-making in robot swarms: Exploiting a third truth state

Crosscombe

Lawry

Hauert

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Cooperative Decision-Making in Decentralized Multiple-Robot Systems: The Best-of-N Problem

Cited by 79 publications

References 24 publications

The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

Incremental Social Learning Applied to a Decentralized Decision-Making Mechanism: Collective Learning Made Faster

Robust distributed decision-making in robot swarms: Exploiting a third truth state

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