Summary of Future Research Avenues For Artificial Intelligence in Digital Gaming: An Exploratory Report, by Markus Dablander
Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report
by Markus Dablander
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research pathways in this study focus on applying state-of-the-art AI methods, particularly deep learning, to digital gaming. The five promising directions include investigating large language models as core engines for game agent modeling, using neural cellular automata for procedural game content generation, accelerating computationally expensive in-game simulations via deep surrogate modeling, leveraging self-supervised learning to obtain useful video game state embeddings, and training generative models of interactive worlds using unlabelled video data. The study aims to outline a curated list of encouraging research directions at the intersection of AI and video games that can inspire more rigorous and comprehensive research efforts in the future. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to use artificial intelligence (AI) in video games. It suggests five ideas for applying AI to make games better. These ideas include using special language models to control game characters, creating game content with neural networks, speeding up game simulations with deep learning, getting useful information from game states with self-supervised learning, and generating interactive worlds with generative models. The study also talks about the challenges of combining advanced AI systems with video games and where more progress is needed. |
Keywords
» Artificial intelligence » Deep learning » Self supervised
