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

1 Pith paper cite this work, alongside 10 external citations. Polarity classification is still indexing.

1 Pith paper citing it
10 external citations · Pith
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.

fields

stat.ME 1

years

2026 1

verdicts

ACCEPT 1

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  • Dirichlet kernel density estimation on the simplex with missing data stat.ME · 2026-03-08 · accept · none · ref 16 · internal anchor

    An inverse-probability-weighted Dirichlet kernel density estimator on the simplex is asymptotically normal under MAR missingness, with bias matching full-data Dirichlet KDE and variance inflated by a propensity factor.