Partial Robustness in Team Formation: Bridging the Gap between Robustness and Resilience

Partial Robustness in Team Formation: Bridging the Gap between Robustness and Resilience
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DOI:
10.5555/3463952.3464086
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发表时间:
2021
期刊:
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影响因子:
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通讯作者:
Nicolas Schwind;Emir Demirovic;Katsumi Inoue;Jean-Marie Lagniez
Nicolas Schwind;Emir Demirovic;Katsumi Inoue;Jean-Marie Lagniez
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其他
文献类型:
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作者:
Nicolas Schwind;Emir Demirovic;Katsumi Inoue;Jean-Marie Lagniez

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团队组建是在覆盖一组技能的同时部署最便宜的代理团队的问题。一旦一个团队已经形成,一些在开始时考虑的代理可能最终有缺陷,一些技能可能会被发现。最近引入了两个解决方案概念,以主动方式处理此问题:一个可以组建一个对变化具有鲁棒性的团队,以便在一些代理损失后,所有技能仍然覆盖;或者一个可以选择可恢复的团队,即,在最坏的情况下,可以通过雇用新的代理来“修复”它,同时保持总体部署成本最小。在本文中,我们介绍了部分鲁棒团队形成(PR-TF)的问题。部分鲁棒性是鲁棒性的一种较弱形式,它保证了在一些代理丢失后一定程度的技能覆盖。本文分析了PR-TF算法的计算复杂度,给出了一个完整的算法,并在一些已有的基准测试和一些新引入的基准测试上,将该算法的性能与现有的鲁棒性和可恢复性的团队组建方法进行了实证比较。部分鲁棒性被证明是一个有趣的权衡概念之间(全)的鲁棒性和可恢复性的计算效率,技能覆盖保证代理损失后,和可修复性。
Team formation is the problem of deploying the least expensive team of agents while covering a set of skills. Once a team has been formed, some of the agents considered at start may be finally defective and some skills may become uncovered. Two solution concepts have been recently introduced to deal with this issue in a proactive manner: one may form a team which is robust to changes so that after some agent losses, all skills remain covered; or one may opt for a recoverable team, i.e., it can be “repaired” in the worst case by hiring new agents while keeping the overall deployment cost minimal. In this paper, we introduce the problem of partially robust team formation (PR-TF). Partial robustness is a weaker form of robustness which guarantees a certain degree of skill coverage after some agents are lost. We analyze the computational complexity of PR-TF, and provide a complete algorithm for it. The performance of our algorithm is empirically compared with the existing methods for robust and recoverable team formation, on a number of existing benchmarks and some newly introduced ones. Partial robustness is shown to be an interesting trade-off notion between (full) robustness and recoverability in terms of computational efficiency, skill coverage guarantees after agent losses, and repairability.