Learning Constructive Primitives for Real-Time Dynamic Difficulty Adjustment in Super Mario Bros
Peizhi Shi, Ke Chen · IEEE Transactions on Games · 2017
Among the main challenges in procedural content generation (PCG), content quality assurance and dynamic difficulty adjustment (DDA) of game content in real time are two major issues concerned in adaptive content generation. Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). To address the quality assurance issue, we exploit the synergy between rule-based and learning-based methods to produce quality game segments in SMB, named constructive primitives (CPs). By means of CPs, we propose a DDA algorithm that controls a CP-based level generator to adjust the content difficulty rapidly based on players' real-time game playing performance. We have conducted extensive simulations with sophisticated SMB agents of different types for thorough evaluation. Experimental results suggest that our approach can effectively assure content quality in terms of generic quality measurements and dynamically adjust game difficulty in real time as informed by the game completion rate.