Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916531079741440 |
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| author | Dablander, Markus |
| author_facet | Dablander, Markus |
| contents | Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as well as providing valuable benchmarks and virtual environments to advance AI technologies in general. This report presents a high-level overview of five promising research pathways for applying state-of-the-art AI methods, particularly deep learning, to digital gaming within the context of the current research landscape. The objective of this work is to outline a curated, non-exhaustive list of encouraging research directions at the intersection of AI and video games that may serve to inspire more rigorous and comprehensive research efforts in the future. We discuss (i) investigating large language models as core engines for game agent modelling, (ii) using neural cellular automata for procedural game content generation, (iii) accelerating computationally expensive in-game simulations via deep surrogate modelling, (iv) leveraging self-supervised learning to obtain useful video game state embeddings, and (v) training generative models of interactive worlds using unlabelled video data. We also briefly address current technical challenges associated with the integration of advanced deep learning systems into video game development, and indicate key areas where further progress is likely to be beneficial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14085 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report Dablander, Markus Machine Learning Artificial Intelligence Human-Computer Interaction Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as well as providing valuable benchmarks and virtual environments to advance AI technologies in general. This report presents a high-level overview of five promising research pathways for applying state-of-the-art AI methods, particularly deep learning, to digital gaming within the context of the current research landscape. The objective of this work is to outline a curated, non-exhaustive list of encouraging research directions at the intersection of AI and video games that may serve to inspire more rigorous and comprehensive research efforts in the future. We discuss (i) investigating large language models as core engines for game agent modelling, (ii) using neural cellular automata for procedural game content generation, (iii) accelerating computationally expensive in-game simulations via deep surrogate modelling, (iv) leveraging self-supervised learning to obtain useful video game state embeddings, and (v) training generative models of interactive worlds using unlabelled video data. We also briefly address current technical challenges associated with the integration of advanced deep learning systems into video game development, and indicate key areas where further progress is likely to be beneficial. |
| title | Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2412.14085 |