Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems -- such as those involving strategic decision-making -- have remained difficult for neuroevolution to solve. This paper evaluates the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. A method for measuring fracture using the concept of function variation is proposed, and based on this concept, two methods for dealing with fracture are examined: neurons with local receptive fields, and refinement based on a cascaded network architecture. Experiments in several benchmark domains are performed to evaluate how different levels of fracture affect the performance of neuroevolution methods, demonstrating that these two modifications improve performance significantly. These results form a promising starting point for expanding neuroevolution to strategic tasks.

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Neural Networks, Special issue on Goal-Directed Neural Systems (2009).

@article{kohl:nn09, title={Evolving Neural Networks for Strategic Decision-Making Problems}, author={Nate Kohl and Risto Miikkulainen}, booktitle={Neural Networks, Special issue on Goal-Directed Neural Systems}, url="http://www.cs.utexas.edu/users/ai-lab?kohl:nn09", year={2009} }