Abstract
There have been a number of suggestions for methodologies supporting the development of multiagent simulation models. In this contribution we are introducing a learning-driven methodology that exploits learning techniques for generating suggestions for agent behavior models based on a given environmental model. The output must be human-interpretable. We compare different candidates for learning techniques – classifier systems, neural networks and reinforcement learning – concerning their appropriateness for such a modeling methodology.
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Junges, R., Klügl, F. (2010). Evaluation of Techniques for a Learning-Driven Modeling Methodology in Multiagent Simulation. In: Dix, J., Witteveen, C. (eds) Multiagent System Technologies. MATES 2010. Lecture Notes in Computer Science(), vol 6251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16178-0_18
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DOI: https://doi.org/10.1007/978-3-642-16178-0_18
Publisher Name: Springer, Berlin, Heidelberg
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