Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The evolution of strategies for multiagent environments
Adaptive Behavior
KidSim: programming agents without a programming language
Communications of the ACM
Introduction to artificial life
Introduction to artificial life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Adaptation and Learning in Multi-Agent Systems: Some Remarks and a Bibliography
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Using Adaptive Multi-Agent Systems to Simulate Economic Models
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Classifier fitness based on accuracy
Evolutionary Computation
Large-Scale Agent-Based Pedestrian Simulation
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
Learning automata as a basis for multi agent reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Evaluation of techniques for a learning-driven modeling methodology in multiagent simulation
MATES'10 Proceedings of the 8th German conference on Multiagent system technologies
Evolution for modeling: a genetic programming framework for sesam
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Generating inspiration for agent design by reinforcement learning
Information and Software Technology
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Although multi-agent simulations are an intuitive way of conceptualizing systems that consist of autonomous actors, a major problem is the actual design of the agent behavior. In this contribution, we examine the potential of using agent-based learning for implementing the agent behavior. We enhanced SeSAm, a platform for agent-based simulation, by replacing the usual rule-based agent architecture by XCS, a well-known learning classifier system (LCS). The resulting model is tested using a simple evacuation scenario. The results show that on the one hand side plausible agent behavior could be learned. On the other hand side, though, the results are quite brittle concerning the frame of environmental feedback, perception and action modeling.