Evaluation of Techniques for a Learning-Driven Modeling Methodology in Multiagent Simulation

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.