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Authors: Guilhem Marcillaud 1 ; Valérie Camps 1 ; Stéphanie Combettes 1 ; Marie-Pierre Gleizes 1 and Elsy Kaddoum 2

Affiliations: 1 Institut de Recherche en Informatique de Toulouse, Université Toulouse III Paul Sabatier, Toulouse, France ; 2 Institut de Recherche en Informatique de Toulouse, Université Toulouse II Jean Jaures, Toulouse, France

Keyword(s): Cross-understanding, Data Imputation, Multi-referential Information, MultiAgent System, Heterogeneous Agents.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 353-360. DOI: 10.5220/0010298503530360

@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},
issn={2184-433X},
}

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
IS - 2184-433X
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
PB - SciTePress

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