Team Sports for Game AI Benchmarking Revisited
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 te...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Computer Games Technology |
| Online Access: | http://dx.doi.org/10.1155/2021/5521877 |
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| _version_ | 1832550571988484096 |
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| author | Maxim Mozgovoy Mike Preuss Rafael Bidarra |
| author_facet | Maxim Mozgovoy Mike Preuss Rafael Bidarra |
| author_sort | Maxim Mozgovoy |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8e6f4423a18943bba1944afbc4dd666f |
| institution | Kabale University |
| issn | 1687-7047 1687-7055 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Computer Games Technology |
| spelling | doaj-art-8e6f4423a18943bba1944afbc4dd666f2025-02-03T06:06:27ZengWileyInternational Journal of Computer Games Technology1687-70471687-70552021-01-01202110.1155/2021/55218775521877Team Sports for Game AI Benchmarking RevisitedMaxim Mozgovoy0Mike Preuss1Rafael Bidarra2The University of Aizu, JapanLIACS, Universiteit Leiden, NetherlandsComputer Graphics and Visualization Group, Delft University of Technology, NetherlandsSport 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.http://dx.doi.org/10.1155/2021/5521877 |
| spellingShingle | Maxim Mozgovoy Mike Preuss Rafael Bidarra Team Sports for Game AI Benchmarking Revisited International Journal of Computer Games Technology |
| title | Team Sports for Game AI Benchmarking Revisited |
| title_full | Team Sports for Game AI Benchmarking Revisited |
| title_fullStr | Team Sports for Game AI Benchmarking Revisited |
| title_full_unstemmed | Team Sports for Game AI Benchmarking Revisited |
| title_short | Team Sports for Game AI Benchmarking Revisited |
| title_sort | team sports for game ai benchmarking revisited |
| url | http://dx.doi.org/10.1155/2021/5521877 |
| work_keys_str_mv | AT maximmozgovoy teamsportsforgameaibenchmarkingrevisited AT mikepreuss teamsportsforgameaibenchmarkingrevisited AT rafaelbidarra teamsportsforgameaibenchmarkingrevisited |