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Communication Dans Un Congrès Année : 2019

GAAMAAA: Generating Automatically an Adaptive Multiagent System for Ambient Assistive Applications

Résumé

The purpose of this paper is to present a clear description of an approach to automate the conception process, to implement and to deploy an open and adaptive multiagent system for personal assistive applications. This kind of applications increasingly relies on a network of connected objects which are present in the environment of elderly or sick people. It deals with exploiting the connected objects of this environment to offer a service (e.g., fall detection, localization) to these persons. Since the environment is dynamic, due to the availability or the lack of connected objects and to the diversity of situations, cooperation mechanisms among connected objects must be dynamically and adaptively designed. The proposed approach emphasizes the cooperation between these different connected objects. This work is based on cooperative multiagent systems in which each agent models a connected object.We will leverage interaction protocols in multiagent systems in order to design a platform capable of generating an adaptive multiagent systems automatically. The adaptation takes into consideration the context of the person as well as the existing interaction protocols. This paper presents an infrastructure which allows the use of ontologies for agent-oriented software engineering. The first results are very promising since they show several advantages of the approach in terms of adaptability of the generated multiagent system, as indicated by the experiments we have carried out.
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Dates et versions

hal-02434303 , version 1 (09-01-2020)

Identifiants

Citer

Amina Maddouri, Nadia Abchiche-Mimouni, Samir Otmane, Jalel Akaichi. GAAMAAA: Generating Automatically an Adaptive Multiagent System for Ambient Assistive Applications. 18th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET 2019), Sep 2019, Kuching, Sarawak, Malaysia. pp.39 --54, ⟨10.3233/FAIA190037⟩. ⟨hal-02434303⟩
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