# Cooperative Multi-agent Approach for Automated Computer Game Testing

arXiv: https://arxiv.org/abs/2405.11347 · HTML: https://mlsanity.com/paper/2405.11347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 5 of 5)

- [Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents](https://mlsanity.com/paper/2103.13798/) (2021)
- [Automated Video Game Testing Using Synthetic and Human-Like Agents](https://mlsanity.com/paper/1906.00317/) (2019)
- [Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing](https://mlsanity.com/paper/2003.07813/) (2020)
- [An Agent-based Approach to Automated Game Testing: an Experience Report](https://mlsanity.com/paper/2211.06386/) (2022)
- [An Online Agent-Based Search Approach in Automated Computer Game Testing with Model Construction](https://mlsanity.com/paper/2211.06936/) (2022)

## Abstract (arXiv)

Automated testing of computer games is a challenging problem, especially when lengthy scenarios have to be tested. Automating such a scenario boils down to finding the right sequence of interactions given an abstract description of the scenario. Recent works have shown that an agent-based approach works well for the purpose, e.g. due to agents' reactivity, hence enabling a test agent to immediately react to game events and changing state. Many games nowadays are multi-player. This opens up an interesting possibility to deploy multiple cooperative test agents to test such a game, for example to speed up the execution of multiple testing tasks. This paper offers a cooperative multi-agent testing approach and a study of its performance based on a case study on a 3D game called Lab Recruits.