# Exploring Gameplay With AI Agents

arXiv: https://arxiv.org/abs/1811.06962 · HTML: https://mlsanity.com/paper/1811.06962/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation](https://mlsanity.com/paper/2107.02457/) (2021)
- [Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees](https://mlsanity.com/paper/2203.12774/) (2022)
- [Rinascimento: searching the behaviour space of Splendor](https://mlsanity.com/paper/2106.08371/) (2021)

## Abstract (arXiv)

The process of playtesting a game is subjective, expensive and incomplete. In this paper, we present a playtesting approach that explores the game space with automated agents and collects data to answer questions posed by the designers. Rather than have agents interacting with an actual game client, this approach recreates the bare bone mechanics of the game as a separate system. Our agent is able to play in minutes what would take testers days of organic gameplay. The analysis of thousands of game simulations exposed imbalances in game actions, identified inconsequential rewards and evaluated the effectiveness of optional strategic choices. Our test case game, The Sims Mobile, was recently released and the findings shown here influenced design changes that resulted in improved player experience.