Circuit-adaptive challenge balancing in racing games

In this paper, we propose a novel approach to challenge balancing in racing games: circuit-adaptive challenge balancing. We propose to automatically adapt the actual racing circuit - while it is being played - such that the performed circuit adaptations intelligently balance the challenge for all players in parallel. Our approach to circuit-adaptive game balancing is submitted as an alternative to the traditional rubber banding method (not a replacement), that may contribute particularly to distinct design goals and gameplay events. Indeed, an interesting feature of the approach, is that each player will be targeted with distinct, player-appropriate circuit adaptations. Consequently, we test the hypothesis that a perceptively balanced game can be achieved via such player-appropriate adaptations. The approach itself is built around (A) a classifier that can assess a player's in-game performance, and (B) an algorithm that employs the ability of targeted circuit adaptations, to the end of realising circuit-based challenge balancing. Experiments that validate the approach - by means of simulation studies and studies with actual human participants - suggest that the approach can automatically balance the challenge in actual racing games, by reducing the gap size between all players in parallel, while ensuring that it does not come at a cost in terms of the actual player experience.