Evolving Multimodal Behavior
Jacob Schrum · 2009
Multimodal behavior occurs when an agent exhibits distinctly different kinds of actions under different circumstances. Many interesting problems in real and simulated environments require agents that exhibit such behavior. The ability to automatically discover multimodal behavior would be useful in robotics, video games and other high-level control problems. Multimodal behavior is also especially important for teams of agents, taking the form of division of labor between team members. This proposed dissertation develops a method for discovering such behavior via neuroevolution. Work completed so far demonstrates how three modifications to typical neuroevolutionary methods make multimodal behavior easier to evolve: (1) multiobjective evolution (via e.g. the multiobjective evolutionary algorithm NSGA-II) encourages multimodal behavior because distinct behaviors tend to be associated with sets of contradictory objectives, (2) whenever the population collectively surpasses preset objective goals, the corresponding objectives can be dropped, speeding up evolution, and (3) a special mutation operator that creates a new set of output neurons for a neural network encourages the development of multiple distinct behavioral