Robust Multi-robot Team Formations Using Weighted Voting Games

Dasgupta, Prithviraj; Cheng, Ke

doi:10.1007/978-3-642-32723-0_27

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…More precisely, we set p = 0 and compare the runtime for finding an optimal coalition structure by varying the value k. Figure 1 represents the average runtime of all distributions for different k (i.e., k = 0, 1, 2, 3, 4, 5 ). As one can see, we observed similar results for all distributions (1)- (6). When the number of agents is small, the influence of k on the runtime is not significant.…”

Section: Preliminary Experimentssupporting

confidence: 78%

“…Lastly, our framework could potentially be extended to a dynamic setting in which the set of agents A may change over time. The objective here would be to apply such an extended framework to the distributed robot team reconfiguration problem [6].…”

Section: Resultsmentioning

confidence: 99%

“…For instance, with the uniform distribution the result of (c) for the parameter p = 0.3 increases from 2.013 to 2.806 when the number of agents increases from 6 to 16, and the result of (d) for the parameter p = 0.7 increases from 3.339 to 5.255. Figure 3 represents the average runtime of and for all distributions (1)- (6). The x-axis shows the number of agents and the y-axis represents the average runtime.…”

Section: Performance Of the Approximation Algorithmsmentioning

confidence: 99%

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Two approximation algorithms for probabilistic coalition structure generation with quality bound

Matsumura

Kodric

Okimoto

et al. 2020

Auton Agent Multi-Agent Syst

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How to form effective coalitions is an important issue in multi-agent systems. Coalition Structure Generation ( ) is a fundamental problem whose formalization can encompass various applications related to multi-agent cooperation.involves partitioning a set of agents into coalitions such that the social surplus (i.e., the sum of the values of all coalitions) is maximized. In traditional , we are guaranteed that all coalitions will be successfully established, that is, the attendance rate of each agent for joining any coalition is assumed to be 1.0. Having the real world in mind, however, it is natural to consider the uncertainty of agents' availabilities, e.g., an agent might be available only two or three days a week because of his/her own schedule. Probabilistic Coalition Structure Generation ( ) is an extension of where the attendance type of each agent is considered. The aim of this problem is to find the optimal coalition structure which maximizes the sum of the expected values of all coalitions. In , since finding the optimal coalition structure easily becomes intractable, it is important to consider approximation algorithms, i.e., to consider a trade-off between the quality of the returned solution and tractability. In this paper, a formal framework for is introduced. Approximation algorithms for called Bounded Approximation Algorithm based on Attendance Types ( ) and Involved ( ) are then presented. We prove a priori bounds on the quality of the solution returned by and with respect to the optimum and perform experimental evaluations on a number of benchmarks.

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Section: Preliminary Experimentssupporting

confidence: 78%

Section: Resultsmentioning

confidence: 99%

Section: Performance Of the Approximation Algorithmsmentioning

confidence: 99%

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Two approximation algorithms for probabilistic coalition structure generation with quality bound

Matsumura

Kodric

Okimoto

et al. 2020

Auton Agent Multi-Agent Syst

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“…One approach uses weighted voting and game theory to determine when to split a group into subgroups as a way to resolve conflicting interests [20]. However, the approach relied on a despotic leader robot that sent commands to other members of the group, a restriction that is impractical as the group is scaled.…”

Section: Related Workmentioning

confidence: 99%

Consensus costs and conflict in a collective movement

Solum

Eskridge

Schlupp

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Aggregation, whether it be in natural or artificial systems, provides numerous benefits to both the individual and the group. However, aggregation has costs and frequently involves inter-individual conflict. Although conflicts in natural systems is understood to be at times beneficial, as well as detrimental, conflict in artificial systems, such as a team of robots, is frequently viewed as inhibiting consensus and, therefore, success. This is particularly the case in large-scale aggregations where ensuring consensus is especially challenging. In response, mechanisms are often integrated into the group's control systems to minimize, or even eliminate, conflicts of interest. As a result, the potential benefits of losing consensus, such as increased diversity and reduced consensus costs, are not available. Using a biologically-based collective movement model, we demonstrate that not enforcing consensus and allowing conflict to evolve as agents make decisions results in a system in which agents meet their own needs, thus minimizing consensus costs, while still maintaining group cohesion when possible. Simulations predict that conflict balances consensus costs with individual preferences such that both individual and group goals are met.

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“…Vidal et al [6] focused on the research domain of task oriented, and shows the benefits of cooperation and selflessness. A distributed algorithm for the reconstruction of robot team is described in [7], take advantage of the function of robot local computing to determine whether a team needs to split or two teams need to reconfigure the process of merging. Kaminka et al [8] introduced a kind of behavior based team architecture.…”

Section: Related Workmentioning

confidence: 99%

A Robust Mechanism for Robot Team Formation

Jiang²,

Li³

et al. 2017

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

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With the development of AI, the job of robots became more complex and diversified, due to the limited capacity of a single robot, multiple robots cooperation is used to accomplish complex tasks. Based on the problem of how to form a cooperative team, in this paper we proposed a robust mechanism for robot team formation (RMTF). First the mechanism designed a SC algorithm for select team coordinator, and then used the CRN algorithm to check teams who can complete the task. Finally, the OT algorithm is used to optimize the team to perform the task. In this paper, an example is given to illustrate the robustness of the mechanism, compared with the existing mechanism of robot team formation, RMTF increased the team's robustness, and ensured the stability of the team to complete the task.

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Robust Multi-robot Team Formations Using Weighted Voting Games

Cited by 12 publications

References 10 publications

Two approximation algorithms for probabilistic coalition structure generation with quality bound

Two approximation algorithms for probabilistic coalition structure generation with quality bound

Consensus costs and conflict in a collective movement

A Robust Mechanism for Robot Team Formation

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