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

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arxiv 2009.00541 v1 pith:U2X5E6EZ submitted 2020-09-01 cs.AI

classification cs.AI
keywords researchchallengesgamesagentagentsgameindustryopportunities
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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.

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Cited by 2 Pith papers

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  1. Dirichlet kernel density estimation on the simplex with missing data

    stat.ME 2026-03 accept novelty 5.0 of 10

    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.

  2. Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation

    cs.LG 2025-08 reject novelty 3.0 of 10

    The authors claim Gaussian-interpolated sparse campus load data enables load forecasting, with LSTM achieving a 10.67% MAPE, but the reported accuracy is measured against the very imputations the method creates.

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