Reinforcement Learning-based Real-time Fair Online Resource Matching

Pankaj Mishra, Pankaj Mishra, Ahmed Moustafa

2022

Abstract

Designing a resource matching policy in an open market paradigm is a challenging and complex problem. The complexity is mainly due to the conflicting objectives of the independent resource providers and dynamically arriving online buyers. In specific, providers aim to maximise their revenue, whereas buyers aim to minimise their resource costs. Therefore, to address this complex problem, there is an immense need for a fair matching platform. In specific, the platform must optimise the pricing rule on behalf of resource providers to maximise their revenue at one end. Then, on the other hand, the broker must fairly match the resource request on behalf of buyers. Owing to this we propose a two-step unbiased broker based resource matching mechanism in the auction paradigm. In the first step, the broker computes optimal trade prices on behalf of the providers using a novel reinforcement learning algorithm. Then, in the second step appropriate provider is matched with the buyer’s request based on a novel multi-criteria winner determination strategy. Towards the end, we compare our online resource matching approach with two existing state-of-the-art algorithms. Then, from the experimental results, we show that the novel matching algorithm outperforms the other two baselines.

Paper Citation

in Harvard Style

Mishra P. and Moustafa A. (2022). Reinforcement Learning-based Real-time Fair Online Resource Matching. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-547-0, pages 34-41. DOI: 10.5220/0010834600003116

in Bibtex Style

@conference{icaart22,
author={Pankaj Mishra and Ahmed Moustafa},
title={Reinforcement Learning-based Real-time Fair Online Resource Matching},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2022},
pages={34-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010834600003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Reinforcement Learning-based Real-time Fair Online Resource Matching
SN - 978-989-758-547-0
AU - Mishra P.
AU - Moustafa A.
PY - 2022
SP - 34
EP - 41
DO - 10.5220/0010834600003116