"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games

TL;DR

This paper explores the challenges faced by industry professionals in creating game agents, comparing industry practices with research, and identifying opportunities for integrating machine learning to improve agent development workflows.

Contribution

It provides an empirical analysis of industry workflows for game agent creation and highlights research opportunities to address identified challenges.

Findings

Industry workflows are mature but face design and implementation challenges.

Research can inform improvements in agent creation processes.

Future directions include integrating ML techniques into industry pipelines.

Abstract

Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games industry evolves to adopt machine learning (ML) more widely, it is vital that the research community learn from the best practices cultivated within the industry over decades creating agents. However, although commercial game agent creation pipelines are more mature than those based on ML, opportunities for improvement still abound. As a foundation for shared progress identifying research opportunities between researchers and practitioners, we interviewed seventeen game agent creators from AAA studios, indie studios, and industrial research labs about the challenges they experienced with their professional workflows. Our study revealed several open challenges ranging from design to implementation and evaluation. We compare with literature from the research community that address the challenges identified and conclude by highlighting promising directions for future research supporting agent creation in the games industry.