Abstract
This article focuses on the issue of semantic interoperability in heterogeneous distributed multi-agent systems. Existing middleware technologies offer programming models that strongly combine agents’ learning models and communication models, which can lead to performance weaknesses when the number of agents is very important. Moreover, existing methods in the field of semantic interoperability solve the problem of understanding messages exchanged between distributed agents with heterogeneous ontologies, using several techniques to combine these ontologies. The first category of these methods relies on the fusion principle, others use alignment, and finally, there are those founded on Semantic Web technique. All these methods are limited to abstract concepts and do not deal with concrete concepts such as those represented by images. We propose in this paper a new approach that addresses the problem of semantic interoperability between heterogeneous distributed agents based on two principles: At first, the communication aspect of the agent from the learning aspect is separated. Then, we propose extending semantic interoperability to concrete concepts by combining two techniques: Semantic Web technology, which allows terms representing abstract concepts to be interpreted and deep learning technology, which is introduced as a new method to ensure semantic interoperability in the case of concrete concepts such as images. A detailed description of the proposed approach is provided, showing that it is very useful in solving the disadvantages of existing multi-agent platforms.
Highlights
Multi-agent systems (MASs) are an interesting solution for the modeling of a complex and massively distributed system.The distributed elements of such a system need to communicate and interact with each other despite their technological heterogeneity
In order to improve semantic interoperability in heterogeneous distributed systems, many techniques for combining these ontologies have been introduced and can be classified into three main categories: First, we find those based on the principle of fusion, others use alignment, and those based on the Semantic Web
We give some definitions of the main concepts and tools utilized in the design of our approach, and we focus on the description of the two technologies we adopted to ensure semantic interoperability: Semantic Web and Deep Learning
Summary
Multi-agent systems (MASs) are an interesting solution for the modeling of a complex and massively distributed system. Various studies have proposed ontologies as a mean to solve the semantic interoperability problem between heterogeneous agents Most of these approaches have presented methods for interpreting the conceptualizations of a given ontology based on mapping, merging [8,9,10] or aligning ontologies [11,12,13]. It would be interesting to be able to deal with concrete objects because of the spectacular evolutions of information technologies: Internet of Things (IoT), mobile applications and social networks In this context, a comprehensive approach is proposed, which addresses the problem of interoperability between heterogeneous agents by using semantic web technology to interpret terms representing abstract concepts and Deep Learning technology to recognize and interpret concrete concepts such as images. The recognition and interpretation of image-type objects would be useful in the automated management of fields like road traffic, for the interpretation of road signs by driverless vehicles, medical imaging diagnostics for anomaly detection, etc
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