  In this paper, we propose a generic framework that enables game developers
without knowledge of machine learning to create bot behaviors with playstyles
that align with their preferences. Our framework is based on interactive
reinforcement learning (RL), and we used it to create a behavior authoring tool
called MarioMix. This tool enables non-experts to create bots with varied
playstyles for the game titled Super Mario Bros. The main interaction procedure
of MarioMix consists of presenting short clips of gameplay displaying
precomputed bots with different playstyles to end-users. Then, end-users can
select the bot with the playstyle that behaves as intended. We evaluated
MarioMix by incorporating input from game designers working in the industry.
