Print Email Facebook Twitter Team Sports for Game AI Benchmarking Revisited Title Team Sports for Game AI Benchmarking Revisited Author Mozgovoy, Maxim (University of Aizu Aizuwakamatsu) Preuss, Mike (Universiteit Leiden) Bidarra, Rafael (TU Delft Computer Graphics and Visualisation) Date 2021 Abstract Sport games are among the oldest and best established genres of computer games. Sport-inspired environments, such as RoboCup, have been used for AI benchmarking for years. We argue that, in spite of the rise of increasingly more sophisticated game genres, team sport games will remain an important testbed for AI benchmarking due to two primary factors. First, there are several genre-specific challenges for AI systems that are neither present nor emphasized in other types of games, such as team AI and frequent replanning. Second, there are unmistakable nonskill-related goals of AI systems, contributing to player enjoyment, that are most easily observed and addressed within a context of a team sport, such as showing creative and emotional traits. We analyze these factors in detail and outline promising directions for future research for game AI benchmarking, within a team sport context. To reference this document use: http://resolver.tudelft.nl/uuid:26894c9c-1564-4f6a-ae5b-a90eddd0a6da DOI https://doi.org/10.1155/2021/5521877 ISSN 1687-7047 Source International Journal of Computer Games Technology, 2021, 1-9 Part of collection Institutional Repository Document type journal article Rights © 2021 Maxim Mozgovoy, Mike Preuss, Rafael Bidarra Files PDF 5521877.pdf 526.98 KB Close viewer /islandora/object/uuid:26894c9c-1564-4f6a-ae5b-a90eddd0a6da/datastream/OBJ/view