Fuzzy Sets and Systems
On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Artificial Intelligence
Machine Learning
A fuzzy consensus aggregation operator
Fuzzy Sets and Systems
Representation and application of fuzzy numbers
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Minimal Model of Communication for a Multi-agent Classifier System
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Cooperative multiagent learning
Adaptive agents and multi-agent systems
Cooperative learning using advice exchange
Adaptive agents and multi-agent systems
Multiagent learning for open systems: a study in opponent classification
Adaptive agents and multi-agent systems
Learning in multiagent systems: an introduction from a game-theoretic perspective
Adaptive agents and multi-agent systems
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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.