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cs.AI2025
Graph Contextual Reinforcement Learning for Efficient Directed Controller Synthesis
Toshihide Ubukata, Enhong Mu, Takuto Yamauchi +3
Controller synthesis is a formal method approach for automatically generating Labeled Transition System (LTS) controllers that satisfy specified properties. The efficiency of the s…
cs.AI2025
Synergizing Code Coverage and Gameplay Intent: Coverage-Aware Game Playtesting with LLM-Guided Reinforcement Learning
Enhong Mu, Minami Yoda, Yan Zhang +3
The widespread adoption of the "Games as a Service" model necessitates frequent content updates, placing immense pressure on quality assurance. In response, automated game testing…
cs.AI2025
Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting
Enhong Mu, Jinyu Cai, Yijun Lu +3
The rapid iteration and frequent updates of modern video games pose significant challenges to the efficiency and specificity of testing. Although automated playtesting methods base…