Learning Human-like Behaviors using NeuroEvolution with Statistical Penalties

In game artificial intelligence (AI), two common directions for developing non-human computer players are strong AI and human-like AI. Human-like AI aims at making computer agents behave like humans. In this direction, NeuroEvolution (NE), which is a combination of an artificial neural network (ANN) and an evolutionary algorithm (EA), had been frequently used to a make computer agent to behave like a human. Our research introduces a novel approach to create human-like computer agents in a platform game Super Mario Bros. (SMB) - we called it a 2D action game in this research. The approach utilizes statistical penalties to evaluate candidates created by NE algorithm. The penalties help in reducing mechanical actions of computer agents based on human data statistics, and the effects of statistical penalties are analyzed by asking human subjects to rate the human-likeness of agents. Experiments show that our method improves the human-likeness in the behavior of a computer agent