Evaluating Quality of Gaming Narratives Co-created with AI

Thu, 04 Sep 2025 00:00:00 +0000

This paper proposes a structured methodology to evaluate AI-generated game narratives, leveraging the Delphi study structure with a panel of narrative design experts. Our approach synthesizes story quality dimensions from literature and expert insights, mapping them into the Kano model framework to understand their impact on player satisfaction. The results can inform game developers on prioritizing quality aspects when co-creating game narratives with generative AI.

AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games

Fri, 08 Aug 2025 00:00:00 +0000

Cheating in online video games compromises the integrity of gaming experiences. Anti-cheat systems, such as VAC (Valve Anti-Cheat), face significant challenges in keeping pace with evolving cheating methods without imposing invasive measures on users systems. This paper presents AntiCheatPT_256, a transformer-based machine learning model designed to detect cheating behaviour in Counter-Strike 2 using gameplay data. To support this, we introduce and publicly release CS2CD: A labelled dataset of 795 matches. Using this dataset, 90,707 context windows were created and subsequently augmented to address class imbalance. The transformer model, trained on these windows, achieved an accuracy of 89.17% and an AUC of 93.36% on an unaugmented test set. This approach emphasizes reproducibility and real-world applicability, offering a robust baseline for future research in data-driven cheat detection.

Paolo Burelli | Andrea De Lorenzo

Evaluating Quality of Gaming Narratives Co-created with AI

AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games