Learning-Based Video Game Development in MLP@UoM: An Overview

TL;DR

This paper reviews recent progress in applying machine learning techniques to streamline and enhance various aspects of video game development, highlighting innovative approaches and future research directions.

Contribution

It provides an overview of recent advancements by MLP@UoM in integrating machine learning into video game development processes.

Findings

Machine learning improves game generation efficiency.

AI-driven agents enhance gameplay and testing.

Future research will expand learning-based development methods.

Abstract

In general, video games not only prevail in entertainment but also have become an alternative methodology for knowledge learning, skill acquisition and assistance for medical treatment as well as health care in education, vocational/military training and medicine. On the other hand, video games also provide an ideal test bed for AI researches. To a large extent, however, video game development is still a laborious yet costly process, and there are many technical challenges ranging from game generation to intelligent agent creation. Unlike traditional methodologies, in Machine Learning and Perception Lab at the University of Manchester (MLP@UoM), we advocate applying machine learning to different tasks in video game development to address several challenges systematically. In this paper, we overview the main progress made in MLP@UoM recently and have an outlook on the future research directions in learning-based video game development arising from our works.