Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Adding temporary memory to ZCS
Adaptive Behavior
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Making Organizational Learning Operational: Implications from Learning Classifier Systems
Computational & Mathematical Organization Theory
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Adaption of XCS to multi-learner predator/prey scenarios
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Learning from others: Exchange of classification rules in intelligent distributed systems
Artificial Intelligence
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An Organizational-learning oriented Classifier System(OCS) is an extension of Learning Classifier Systems (LCSs) to multiagent environments, introducing the concepts of organizational learning (OL) in organization and management science. Unlike conventional research on LCSs which mainly focuses on single agent environments, OCS has an architecture for addressing multiagent environments. Through intensive experiments on a complex scalable domain, the following implications have been revealed: (1) OCS finds good solutions at small computational costs in comparison with conventional LCSs, namely the Michigan and Pittsburgh approaches; (2) the learning mechanisms at the organizational level contribute to improving the performance in multiagent environments; (3) an estimation of environmental situations and utilization of records of past situations/actions must be implemented at the organizational level to cope with non-Markov properties in multiagent environments.