Abstract
Intelligent organisms do not simply perform one task, but exhibit multiple distinct modes of behavior. For instance, humans can swim, climb, write, solve problems, and play sports. To be fully autonomous and robust, it would be advantageous for artificial agents, both in physical and virtual worlds, to exhibit a similar diversity of behaviors. Artificial evolution, in particular neuroevolution [3, 4], is known to be capable of discovering complex agent behavior. This dissertation expands on existing neuroevolution methods, specifically NEAT (Neuro-Evolution of Augmenting Topologies [7]), to make the discovery of multiple modes of behavior possible. More specifically, it proposes four extensions: (1) multiobjective evolution, (2) sensors that are split up according to context, (3) modular neural network structures, and (4) fitness-based shaping. All of these technical contributions are incorporated into the software framework of Modular Multiobjective NEAT (MM-NEAT), which can be downloaded here.
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