Adaptive rubber-banding system of dynamic difficulty adjustment in racing games

Abstract

Dynamic difficulty adjustment (DDA) is a highly significant research direction in game AI. Rubber-banding is one of the DDA techniques extensively used in racing games. This paper improves the poor adaptability of the rubber-banding system combined with the game design and development techniques. An Adaptive Rubber-Banding System (ARBS) based on ARBS relationship curve, modular mechanism, as well as detail processing schemes, is implemented in Unreal Engine 4. The paper builds various test programs and compares each system in terms of the number of players, gameplay mode, and track distance. Experimental results demonstrate that ARBS is more adaptive and provides a better experience for players. Game designers and developers are thus able to employ ARBS to design and implement AI systems suitable for racing games effectively.