AI in (and for) Games

TL;DR

This chapter explores the dual role of AI/ML in games, highlighting how they generate data and enhance game design through automation, content creation, and player behavior prediction.

Contribution

It provides a comprehensive overview of AI/ML applications in gaming, emphasizing their benefits for data collection, content generation, and player experience estimation.

Findings

AI/ML enables large-scale data collection of player behavior.

AI algorithms automate game testing and content generation.

Predictive models improve understanding of player experience.

Abstract

This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.