p739-turner.pdf (1.06 MB)
Distributed strategy adaptation with a prediction function in multi-agent task allocation
conference contribution
posted on 2018-05-21, 13:32 authored by Joanna Turner, Qinggang MengQinggang Meng, Gerald SchaeferGerald Schaefer, Andrea SoltoggioAndrea SoltoggioCoordinating 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
2018-01-24Publication date
2018Notes
This is a conference paper. The AAMAS 2018 Proceedings are archived in the IFAAMAS repository at http://www.ifaamas.org/Proceedings/aamas2018/.ISBN
9781450356497ISSN
2523-5699Publisher version
Language
- en