# AI in (and for) Games

arXiv: https://arxiv.org/abs/2105.03123 · HTML: https://mlsanity.com/paper/2105.03123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 77 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 10 of 10)

- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [STARDATA: A StarCraft AI Research Dataset](https://mlsanity.com/paper/1708.02139/) (2017)
- [Machine learning and semantic analysis of in-game chat for cyberbullying](https://mlsanity.com/paper/1907.10855/) (2019)
- [Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model](https://mlsanity.com/paper/1710.02262/) (2017)
- [Between an Arena and a Sports Bar: Online Chats of eSports Spectators](https://mlsanity.com/paper/1801.02862/) (2018)
- [Developing for personalised learning: the long road from educational objectives to development and feedback](https://mlsanity.com/paper/2101.11333/) (2021)
- [There is more to PCG than Meets the Eye: NPC AI, Dynamic Camera, PVS and Lightmaps](https://mlsanity.com/paper/1808.00328/) (2018)
- [A Security Case Study for Blockchain Games](https://mlsanity.com/paper/1906.05538/) (2019)
- [Open and Cultural Data Games for Learning](https://mlsanity.com/paper/2004.07521/) (2020)
- [Shimon the Robot Film Composer and DeepScore: An LSTM for Generation of Film Scores based on Visual Analysis](https://mlsanity.com/paper/2011.07953/) (2020)

## Abstract (arXiv)

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.