Cooperative Multi-agent Approach for Automated Computer Game Testing

TL;DR

The paper tackles scalable automated testing of long, complex computer games by introducing a cooperative multi-agent testing framework. It combines autonomous, reactive agents with dynamic task allocation, information synchronization, and resource locking to coordinate testing tasks on a single game instance. An experimental case study on Lab Recruits shows that extended information sharing between two agents consistently outperforms single-agent and basic-sharing setups, achieving notable speedups (e.g., up to ~35% faster on a 100×100 level) and enabling more tasks to be completed within time budgets. The work demonstrates that multi-agent cooperation can efficiently cover diverse testing tasks in challenging game logic scenarios and outlines future directions for broader case studies and more sophisticated synchronization strategies.

Abstract

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.

Figures (1)

• Figure 1: Two simple levels of a hypothetical game.