Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report
Markus Dablander

TL;DR
This paper explores five promising AI research directions in digital gaming, focusing on deep learning applications like language models, procedural content generation, surrogate modelling, self-supervised learning, and generative models, to enhance game development and AI benchmarking.
Contribution
It provides a curated overview of five innovative AI research pathways in digital gaming, highlighting new applications of deep learning for game content, agent modelling, and simulation acceleration.
Findings
Large language models as game agents
Neural cellular automata for content generation
Deep surrogate models for simulation speedup
Abstract
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…
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Taxonomy
TopicsEducational Games and Gamification · FinTech, Crowdfunding, Digital Finance · Artificial Intelligence in Games
