Abstract
This paper describes the fundamentals of a research project which is being launched in the emerging field of Ambient Intelligence as defined by the European Union’s 6th Research Program on Information Society. Massively multi-agent systems is the natural technique for implementing Ambient Intelligence. Adaptivity is one of the key features of ambient systems. Ensuring that the evolution of an ambient system is predictable and desirable is a challenging open design issue. We propose a user-driven approach to adaptation. We call it “Adaptive Modeling” because it relies on the architectural style known as Adaptive Object-Models. This provides us with a design method and tool for agents to be used in this context. Systems built with this method allow non-programmer domain experts to locally modify the structure and behavior of agents at runtime, and thus obtain system-level adaptation. Expert-driven adaptation should ensure the appropriateness of the system’s behavior with respect to its requirements. We illustrate our method with an existing multi-agent system. Work is under way for extending it with other features, notably fault-tolerance, as well as “agent-driven adaptation” by replacing expert users with monitoring agents endowed with the same expertise.
The work communicated in this paper has been conducted while the first author doing his PhD at Laboratoire d’Informatique de Paris 6 (LIP6), Université Paris 6 – CNRS, Paris, France.
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Razavi, R., Perrot, JF., Guelfi, N. (2005). Adaptive Modeling: An Approach and a Method for Implementing Adaptive Agents. In: Ishida, T., Gasser, L., Nakashima, H. (eds) Massively Multi-Agent Systems I. MMAS 2004. Lecture Notes in Computer Science(), vol 3446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11512073_10
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DOI: https://doi.org/10.1007/11512073_10
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