Fuzzy Utility AI for Handling Uncertainty in Video Game Bots Implementation

Utility AI is an effective tool used to design the behavior of AI agents in games and other multi-agent settings. Lately, it has been gaining a lot of attention in the video games domain, whereby computer players have to act quickly and maintain balance between the effectiveness (i.e., being strong) and the narrative goals (i.e., fulfilling a particular role). This paper presents a novel extension of Utility AI called Fuzzy Utility AI for game environments with uncertain and incomplete information. The main introduced ideas are fuzzy considerations, that generalize the standard considerations in Utility AI, and a new, uncertainty-aware, formula of calculating the utility score. Our intention was to compare the baseline approach with Fuzzy Utility AI using a rudimentary example (a testbed) that is simple to follow and also isolates all external factors that could influence the performance. The empirical results - with a dataset included - show that Fuzzy Utility AI is a more effective approach in environments with incomplete information that needs to be predicted with uncertainty. In the paper, we further confirm the empirical findings with a theoretical analysis.