Daffodil International University
Faculty of Science and Information Technology => Game Design => MCT => Games Model. => Topic started by: S. M. Monowar Kayser on April 15, 2026, 12:46:21 AM
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Game models were traditionally constructed through explicit formalism: finite-state machines, utility systems, search-based procedural content generation, handcrafted rule sets, and designer-authored balance heuristics that privileged interpretability over expressive breadth. The AI revolution has altered that equilibrium by making it feasible to learn generative priors over rules, levels, play traces, and even environment dynamics from large corpora rather than specifying them entirely by hand. Risi and Togelius (2020) argued that procedural content generation should be treated not only as an authoring technique but also as a route toward broader machine learning generality, and that position has become more consequential after 2020 as large language models began synthesizing game rules and level structures jointly rather than assuming fixed mechanics. Hu et al. (2024), for example, showed that LLMs can generate both Video Game Description Language rules and compatible level content, expanding game modeling from content completion to systemic design, while Valevski et al. (2024) demonstrated that diffusion-based world models can learn visually convincing interactive rollouts. Yet this shift should not be romanticized: traditional symbolic models still outperform generative systems whenever solvability guarantees, balance constraints, or deterministic debugging are required, whereas AI-driven models often optimize surface plausibility rather than causal soundness. The major research gap is that current evaluation frameworks rarely test the joint properties that matter most in games, namely playability, explainability, controllability, novelty, and long-horizon balance. Future research should therefore move toward neuro-symbolic game models that expose editable latent abstractions, integrate formal verification with generative priors, and measure quality through designer-facing criteria rather than visual impressiveness alone, because the central question is no longer whether AI can generate a game model, but whether it can generate one that remains legible, tunable, and robust under real design iteration (Risi & Togelius, 2020; Hu et al., 2024; Valevski et al., 2024).
References
1. Risi, S., & Togelius, J. (2020). Increasing generality in machine learning through procedural content generation. Nature Machine Intelligence, 2, 428-436.
2. Hu, C., Zhao, Y., & Liu, J. (2024). Game generation via large language models. In Proceedings of the 2024 IEEE Conference on Games (CoG 2024).
3. Valevski, D., Leviathan, Y., Arar, M., & Fruchter, S. (2024). Diffusion models are real-time game engines. arXiv preprint arXiv:2408.14837.
S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/ (https://monowarkayser.com/)