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GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
13 years 11 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...
ICTAI
2003
IEEE
14 years 3 months ago
Q-Concept-Learning: Generalization with Concept Lattice Representation in Reinforcement Learning
One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use al...
Marc Ricordeau
FPLAY
2008
13 years 12 months ago
Interactive accounting simulation environment for accounting education and training
In contrast to traditional teaching-and-learning environments whereby the teacher controls the learning (e.g., teacher-centered), video games present a learner-centered approach t...
Andrew Milner, Andrew Hogue, Bill Kapralos, John F...
IADIS
2008
13 years 11 months ago
Towards Mining for Influence in a Multi Agent Environment
Multi agent learning systems pose an interesting set of problems: in large environments agents may develop localised behaviour patterns that are not necessarily optimal; in a pure...
Robert Logie, Jon G. Hall, Kevin G. Waugh
ADVCS
2007
108views more  ADVCS 2007»
13 years 10 months ago
Open Synchronous Cellular Learning Automata
Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and als...
Hamid Beigy, Mohammad Reza Meybodi