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
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
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 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
Fuzzy Sets and Systems
Cooperative learning using advice exchange
Adaptive agents and multi-agent systems
Learning in multiagent systems: an introduction from a game-theoretic perspective
Adaptive agents and multi-agent systems
Decision fusion for postal address recognition using belief functions
Expert Systems with Applications: An International Journal
Secure communication for electronic business applications in mobile agent networks
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The cooperative learning systems (COLS) are an interesting way of research in Artificial Intelligence. This is because an intelligence form can emerge by interacting simple agents in these systems. In literature, we can find many learning techniques, which can be improved by combining them to a cooperative learning, this one can be considered as a special case of bagging. In particular, learning classifier systems (LCS) are 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 in consideration the nature of information manipulated by the agents. In this paper we investigate a cooperative learning system based on the Evidential Classifier System, the cooperative system uses Dempster-Shafer theory as a support to make data fusion. This is due to the fact that the Evidential Classifier System is itself based on this theory. We present some ways to make cooperation by using this architecture and discuss the characteristics of such architecture by comparing the obtained results with those obtained by an individual approach.