LIP6 1998/041
- Reports
Une approche méthodologique de la Conception de Systémes Multi-Agents Apprenant - A. Drogoul, J.-D. Zucker
- 30 pages - 10/26/1998- document en - http://www.lip6.fr/lip6/reports/1998/lip6.1998.041.ps.gz - 337 Ko
- Contact : Alexis.Drogoul (at) nulllip6.fr
- Ancien Thème : LIP6
- Keywords : agent-oriented design, distributed machine learning, RoboCup Challenge, collective robotics
- Publisher : Valerie.Mangin (at) nulllip6.fr
This paper deals with one of the probably most challenging and, in our opinion, little addressed question that can be found in Distributed Artificial Intelligence today, that of the methodological design of a learning multi-agent system (MAS). In previous work, in order to solve the current software engineering problem of having the ingredients (MAS techniques) but not the recipes (the methodology) we have defined Cassiopeia, an agent-oriented, role-based method for the design of MAS. It relies on three important notions: (1) independence from the implementation techniques; (2) definition of an agent as a set of three different levels of roles; (3) specification of a methodological process that reconciles both the bottom-up and the top-down approaches to the problem of organization. In this paper we show how this method enables Machine Learning (ML) techniques to be clearly classified and integrated at first hand in the design process of an MAS, by carefully considering the different levels of behaviors to which they can be applied and the techniques which appear to be best suited in these cases. This presentation
allows us to take a broad perspective on the use of all the various techniques developed in ML and their potential use within an MAS design methodology. These techniques are illustrated by examples taken from the RoboCup challenge. We then show that a large part of the design activity is nevertheless left to be done as a result of heuristic choices or experimental work. This allows us to propose the Andromeda framework, which consists in a tighter integration of ML techniques within Cassiopeia itself, in order to assist the designer along the different steps of the method and to develop self-improving MAS.
allows us to take a broad perspective on the use of all the various techniques developed in ML and their potential use within an MAS design methodology. These techniques are illustrated by examples taken from the RoboCup challenge. We then show that a large part of the design activity is nevertheless left to be done as a result of heuristic choices or experimental work. This allows us to propose the Andromeda framework, which consists in a tighter integration of ML techniques within Cassiopeia itself, in order to assist the designer along the different steps of the method and to develop self-improving MAS.