#Supertux scripting reference how to#
Certainly, the Theory of Flow is the most referred in the game AI area and, still, it is unclear how to effectively design and implement adaptive game modules or understanding which game features drive players to a flow state.
Player-centered approaches that aim to maximize player enjoyment have been steady, but with poor heuristics that do not rely on any particular theory of entertainment. 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. We have conducted extensive simulations with sophisticated SMB agents of different types for thorough evaluation. 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.
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).
Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). 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.
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