# On Automating Video Game Regression Testing by Planning and Learning

arXiv: https://arxiv.org/abs/2402.12393 · HTML: https://mlsanity.com/paper/2402.12393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [Automated Video Game Testing Using Synthetic and Human-Like Agents](https://mlsanity.com/paper/1906.00317/) (2019)
- [Safe Learning of Lifted Action Models](https://mlsanity.com/paper/2107.04169/) (2021)
- [Using Reinforcement Learning for Load Testing of Video Games](https://mlsanity.com/paper/2201.06865/) (2022)
- [MACQ: A Holistic View of Model Acquisition Techniques](https://mlsanity.com/paper/2206.06530/) (2022)

## Built on by (most cited first, 1 of 1)

- [Leveraging LLM Agents for Automated Video Game Testing](https://mlsanity.com/paper/2509.22170/) (2025)

## Abstract (arXiv)

In this paper, we propose a method and workflow for automating regression testing of certain video game aspects using automated planning and incremental action model learning techniques. The basic idea is to use detailed game logs and incremental action model learning techniques to maintain a formal model in the planning domain description language (PDDL) of the gameplay mechanics. The workflow enables efficient cooperation of game developers without any experience with PDDL or other formal systems and a person experienced with PDDL modeling but no game development skills. We describe the method and workflow in general and then demonstrate it on a concrete proof-of-concept example -- a simple role-playing game provided as one of the tutorial projects in the popular game development engine Unity. This paper presents the first step towards minimizing or even eliminating the need for a modeling expert in the workflow, thus making automated planning accessible to a broader audience.