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Journal Articles Applied Artificial Intelligence Year : 2005

The STROBE Model: Dynamic Service Generation on the Grid

Abstract

This article presents the STROBE model: both an agent representation and an agent communication model based on a social approach, that means interaction centred. This model represents how agents may realise the interactive, dynamic generation of services on the Grid. Dynamically generated services embody a new concept of service implying a collaborative creation of knowledge i.e. learning; services are constructed interactively between agents depending on a conversation. The approach consists of integrating selected features from Multi-Agent Systems and agent communication, language interpretation in applicative/functional programming and e-learning/human-learning into a unique, original and simple view that privileges interactions, yet including control. The main characteristic of STROBE agents is that they develop a language (environment + interpreter) for each of their interlocutors. The model is inscribed within a global approach, defending a shift from the classical algorithmic (control based) view to problem solving in computing to an interaction-based view of Social Informatics, where artificial as well as human agents operate by communicating as well as by computing. The paper shows how the model may not only account for the classical communicating agent approaches, but also represent a fundamental advance in modelling societies of agents in particular in Dynamic Service Generation scenarios such as those necessary today on theWeb and proposed tomorrow for the Grid. Preliminary concrete experimentations illustrate the potential of the model; they are significant examples for a very wide class of computational and learning situations.
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Dates and versions

lirmm-00105302 , version 1 (11-10-2006)

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Clement Jonquet, Stefano A. Cerri. The STROBE Model: Dynamic Service Generation on the Grid. Applied Artificial Intelligence, 2005, Special issue on Learning Grid Services, 19 (9-10), pp.967-1013. ⟨10.1080/08839510500234826⟩. ⟨lirmm-00105302⟩
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