An evidential and genetic method for cooperative learning systems

Toufik Benouhiba 1 Jean-Marc Nigro 1
1 M2S - Modélisation et Sûreté des Systèmes
ISTIT - Institut des Sciences et Technologies de l'Information de Troyes
Abstract : Cooperative learning systems (COLS) are an interesting research area in Artificial Intelligence because an intelligence form can emerge by simply interacting agents. In literature, there are many learning techniques which can be improved by combining them to a cooperative learning as in bagging. Learning classifier systems (LCS) are particularly adapted to cooperative learning systems because LCS manipulate rules and, hence, knowledge exchange between agents is facilitated. However, a COLS has to use a combination mechanism in order to aggregate information exchanged between agents, this combination mechanism must take into consideration the nature of the manipulated information by agents. This paper presents a cooperative learning system based on the Evidential Classifier System. The cooperative system uses Dempster-Shafer theory as a support to make data fusion due to the fact that the Evidential Classifier System is itself based on this theory. The paper investigates some methods to make cooperation in this system and discusses the characteristics of this latter by comparing the obtained results with those obtained by an individual approach.
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Submitted on : Thursday, September 26, 2019 - 11:22:03 AM
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Toufik Benouhiba, Jean-Marc Nigro. An evidential and genetic method for cooperative learning systems. Multiagent and Grid Systems - An International Journal of Cloud Computing , IOS Press, 2005, 1 (1), pp.29-40. ⟨10.3233/MGS-2005-1104⟩. ⟨hal-02297530⟩

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