A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems

Guilhem Marcillaud, Valérie Camps, Stéphanie Combettes, Marie-Pierre Gleizes, Elsy Kaddoum

2021

Abstract

We propose a self-adaptive module, called LUDA (Learning Usefulness of DAta) to tackle the problem of cross-understanding in heterogeneous multiagent systems. In this work heterogeneity concerns the agents usage of information available under different reference frames. Our goal is to enable an agent to understand other agents information. To do this, we have built the LUDA module analysing redundant information to improve their accuracy. The closest domains addressing this problem are feature selection and data imputation. Our module is based on the relevant characteristics of these two domains, such as selecting a subset of relevant information and estimating the missing data value. Experiments are conducted using a large variety of synthetic datasets and a smart city real dataset to show the feasibility in a real scenario. The results show an accurate transformation of other information, an improvement of the information use and relevant computation time for agents decision making.

Paper Citation

in Harvard Style

Marcillaud G., Camps V., Combettes S., Gleizes M. and Kaddoum E. (2021). A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-484-8, pages 353-360. DOI: 10.5220/0010298503530360

in Bibtex Style

@conference{icaart21,
author={Guilhem Marcillaud and Valérie Camps and Stéphanie Combettes and Marie-Pierre Gleizes and Elsy Kaddoum},
title={A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2021},
pages={353-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010298503530360},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems
SN - 978-989-758-484-8
AU - Marcillaud G.
AU - Camps V.
AU - Combettes S.
AU - Gleizes M.
AU - Kaddoum E.
PY - 2021
SP - 353
EP - 360
DO - 10.5220/0010298503530360