Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Learning Classifier Systems Meet Multiagent Environments
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
On the Rationality of Profit Sharing in Multi-Agent Reinforcement Learning
ICCIMA '01 Proceedings of the Fourth International Conference on Computational Intelligence and Multimedia Applications
Exploring XCS in multiagent environments
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
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Learning classifier systems (LCSs) are rule-based evolutionary reinforcement learning systems. Today, especially variants of Wilson's extended classifier system (XCS) are widely applied for machine learning. Despite their widespread application, LCSs have drawbacks, e. g., in multi-learner scennarios, since the Markov property is not fulfilled. In this paper, LCSs are investigated in an instance of the generic homogeneous and non-communicating predator/prey scenario. A group of predators collaboratively observe a (randomly) moving prey as long as possible, where each predator is equipped with a single, independent XCS. Results show that improvements in learning are achieved by cleverly adapting a multi-step approach to the characteristics of the investigated scenario. Firstly, the environmental reward function is expanded to include sensory information. Secondly, the learners are equipped with a memory to store and analyze the history of local actions and given payoffs.