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Learning Constructive Primitives for Real-time
Dynamic Difficulty Adjustment in Super Mario Bros
ABSTRACT
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 suggested by the game
completion rate.
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