Human-Like Combat Behaviour via Multiobjective Neuroevolution

Although evolution has proven to be a powerful search method for discovering effective behaviour for sequential decision-making problems, it seems unlikely that evolving for raw performance could result in behaviour that is distinctly human-like. This chapter demonstrates how human-like behaviour can be evolved by restricting a bot’s actions in a way consistent with human limitations and predilections. This approach evolves good behaviour, but assures that it is consistent with how humans behave. The approach is demonstrated in the \({UT{\char 94}2}\) bot for the commercial first-person shooter videogame Unreal Tournament 2004. \({UT{\char 94}2}\) ’s human-like qualities allowed it to take second place in BotPrize 2010, a competition to develop human-like bots for Unreal Tournament 2004. This chapter analyzes \({UT{\char 94}2}\) , explains how it achieved its current level of humanness, and discusses insights gained from the competition results that should lead to improved human-like bot performance in future competitions and in videogames in general.