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

Learning-Based Video Game Development in MLP@UoM: An Overview * Ke Chen, Senior Member, IEEE Department of Computer Science, The University of Manchester, Manchester M13 9PL, U.K. Email: Ke.Chen@manchester.ac.uk 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. T o 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. I NTRODUCTION The video games industry has drastically grown since its inception and even surpassed the size of the film industry in 2004. Nowadays, the global revenue of the video industry still rises and increases, and the widespread availability of high-end graphics hardware have resulted in a demand for more complex video games. This in turn has increased the complexity of game development. 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. From an academic perspective, video games also provide an ideal test bed, which allows for researching into automatic video game development and testing new AI algorithms in such a complex yet well-structured environment with ground-truth.