# Optimists at Heart: Why Do We Research Game AI? (Extended Version)

arXiv: https://arxiv.org/abs/2206.03261 · HTML: https://mlsanity.com/paper/2206.03261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 9 of 9)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Deep Learning for Procedural Content Generation](https://mlsanity.com/paper/2010.04548/) (2020)
- [The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integrity](https://mlsanity.com/paper/2009.13676/) (2020)
- [A narrowing of AI research?](https://mlsanity.com/paper/2009.10385/) (2020)
- [Interactive Explanations: Diagnosis and Repair of Reinforcement Learning Based Agent Behaviors](https://mlsanity.com/paper/2105.12938/) (2021)
- [Towards Friendly Mixed Initiative Procedural Content Generation: Three Pillars of Industry](https://mlsanity.com/paper/2005.09324/) (2020)
- [The Social Responsibility of Game AI](https://mlsanity.com/paper/2105.15122/) (2021)
- [iNNk: A Multi-Player Game to Deceive a Neural Network](https://mlsanity.com/paper/2007.09177/) (2020)

## Abstract (arXiv)

In this paper we survey the motivations behind contemporary game AI research by analysing individual publications, the researchers themselves, and the institutions that influence them. In doing so, we identify some negative effects on our field, caused both by external forces outside of our control as well as institutionalised behaviours that are easily overlooked. We suggest how we might begin to address some of these issues as a community, and reassert ourselves as the primary driving force behind the field.