Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning

TL;DR

This paper introduces a data-driven imitation learning approach for automated game validation and testing, enabling designers to efficiently train testing agents with minimal effort and no machine learning expertise.

Contribution

It presents a novel, easy-to-use imitation learning method for game testing that requires little effort and no programming knowledge, validated through industry expert surveys.

Findings

The method is validated as effective for game validation.

Designers find the approach reduces effort and improves testing quality.

Several open challenges and future research directions are identified.

Abstract

In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible. This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming, that designers can use to efficiently train game testing agents. We investigate the validity of our approach through a user study with industry experts. The survey results show that our method is indeed a valid approach to game validation and that data-driven programming would be a useful aid to reducing effort and increasing quality of modern playtesting. The survey also highlights several open challenges. With the help of the most recent literature, we analyze the identified challenges and propose future research directions suitable for supporting and maximizing the utility of our approach.