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Incentivizing Cooperation between Heterogeneous Agents in Dynamic Task Allocation

Authors:

Sofia Amador Nelke,

Roie ZivanAuthors Info & Claims

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

Pages 1082 - 1090

Published: 08 May 2017 Publication History

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Abstract

Market Clearing is an economic concept that features attractive properties when used for resource and task allocation, e.g., Pareto optimality and Envy Freeness. Recently, an algorithm based on Market Clearing, FMC_TA, has been shown to be most effective for realistic dynamic multi agent task allocation, outperforming general optimization methods, e.g., Simulated annealing, and dedicated algorithms, specifically designed for task allocation. That been said, FMC_TA was applied to a homogeneous team of agents and used linear personal utility functions for representing agents' preferences. These properties limited the settings on which the algorithm could be applied.

In this paper we advance the research on task allocation methods based on market clearing by enhancing the FMC_TA algorithm such that it: 1) can use concave personal utility functions as its input and 2) can apply to applications which require the collaboration of heterogeneous agents, i.e. agents with different capabilities. We demonstrate that the use of concave functions indeed encourages collaboration among agents. Our results on both homogeneous and heterogeneous scenarios indicate that the use of personal utility functions with small concavity is enough to achieve the desired incentivized cooperation result, and on the other hand, in contrast to functions with increased concavity, does not cause a severe delay in the execution of tasks.

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Cited By

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Brilliantova APletenev AHosseini HElkind EVeloso MAgmon NTaylor M(2019)The Rise and Fall of Complex Family StructuresProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331939(1847-1849)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331939

Index Terms

Incentivizing Cooperation between Heterogeneous Agents in Dynamic Task Allocation
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination

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Published In

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

May 2017

1914 pages

General Chairs:
Kate Larson
University of Waterloo, Canada
,
Michael Winikoff
University of Otago, New Zealand
,
Program Chairs:
Sanmay Das
Washington University in St. Louis, USA
,
Edmund Durfee
University of Michigan, USA

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ACM: Association for Computing Machinery

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 08 May 2017

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AAMAS '17 Paper Acceptance Rate 127 of 457 submissions, 28%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Brilliantova APletenev AHosseini HElkind EVeloso MAgmon NTaylor M(2019)The Rise and Fall of Complex Family StructuresProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331939(1847-1849)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331939

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Incentive ratio: A game theoretical analysis of market equilibria

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