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Distributed strategy adaptation with a prediction function in multi-agent task allocation

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conference contribution
posted on 21.05.2018 by Joanna Turner, Qinggang Meng, Gerald Schaefer, Andrea Soltoggio
Coordinating multiple agents to complete a set of tasks under time constraints is a complex problem. Distributed consensus-based task allocation algorithms address this problem without the need for human supervision. With such algorithms, agents add tasks to their own schedule according to specified allocation strategies. Various factors, such as the available resources and number of tasks, may affect the efficiency of a particular allocation strategy. The novel idea we suggest is that each individual agent can predict locally the best task inclusion strategy, based on the limited task assignment information communicated among networked agents. Using supervised classification learning, a function is trained to predict the most appropriate strategy between two well known insertion heuristics. Using the proposed method, agents are shown to correctly predict and select the optimal insertion heuristic to achieve the overall highest number of task allocations. The adaptive agents consistently match the performances of the best non-adaptive agents across a variety of scenarios. This study aims to demonstrate the possibility and potential performance benefits of giving agents greater decision making capabilities to independently adapt the task allocation process in line with the problem of interest.

History

School

  • Science

Department

  • Computer Science

Published in

17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018).

Citation

TURNER, J. ... et al, 2018. Distributed strategy adaptation with a prediction function in multi-agent task allocation. IN: Dastani, M. ... et al (eds). Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, Sweden, 10-15 July 2018, pp. 739-747.

Publisher

Association for Computing Machinery (ACM) © International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

Version

NA (Not Applicable or Unknown)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

24/01/2018

Publication date

2018

Notes

This is a conference paper. The AAMAS 2018 Proceedings are archived in the IFAAMAS repository at http://www.ifaamas.org/Proceedings/aamas2018/.

ISBN

9781450356497

ISSN

2523-5699

Language

en

Location

Stockholm, Sweden

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