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Team Sports for Game AI Benchmarking Revisited

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Team Sports for Game AI Benchmarking Revisited Hindawi International Journal of Computer Games Technology Volume 2021, Article ID 5521877, 9 pages https://doi.org/10.1155/2021/5521877 Research Article Team Sports for Game AI Benchmarking Revisited Maxim Mozgovoy ,1 Mike Preuss ,2 and Rafael Bidarra 3 1The University of Aizu, Japan 2LIACS, Universiteit Leiden, Netherlands 3Computer Graphics and Visualization Group, Delft University of Technology, Netherlands Correspondence should be addressed to Rafael Bidarra; [email protected] Received 16 January 2021; Accepted 29 April 2021; Published 31 May 2021 Academic Editor: Michael J. Katchabaw Copyright © 2021 Maxim Mozgovoy et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. 1. Introduction teams playing against each other, each composed of a set of players with their individual roles and abilities” [2]. If further Historically, artificial intelligence (AI) researchers have often specification is desired on what makes up a team in contrast relied on popular games as testbeds for evaluating new algo- to a group, we may go with the definition given by Thompson rithms and approaches. Discussing a famous adage “chess is a (from a management perspective) [3]: “a team is a group of Drosophila of AI”, J. McCarthy wrote: “To computer scien- people who are interdependent with respect to information, tists in general I have only one wish to express: let there be resources, and skills and who seek to combine their efforts to more of these Drosophila-like experiments; let us create some achieve a common goal”. What is not explicitly said here but more specific examples!” [1]. clear from the context is that there must be ways of communi- Several factors may contribute to the success of a partic- cation between the team members. If we add communication ular testbed. For example, in the case of chess, one may note to the team sport definition above, we may get a more com- that the game is easy to set up, it has a wide appeal both to plete definition: team sports are games that involve at least general public and to researchers themselves, it poses prob- two teams playing against each other, each composed of a set lems that are perceived as generalizable beyond the game of players with their individual roles and abilities who cooperate world, and even allows independent study of isolated aspects by means of communication in order to win the game. of the game such as endings. These features make team sports an interesting chal- We can say that most team sports have similar features: lenge for a game AI system, and we, therefore, propose to they are popular among the general public, they are easy to consider them as a promising testbed for AI benchmarking. set up, and they require the participating team members to In the remainder of this paper, we outline our vision on the exhibit both athletic abilities and a certain level of tactical topics involved. and strategic thinking. Although various definitions of team Before suggesting to focus AI benchmarking more on the sport can be given, we propose to abide here by that put for- team sport area, let us first have a look at how the competi- ward in a recent survey: “a game that typically involves two tion landscape currently evolves. Without doubt, game- 2 International Journal of Computer Games Technology related benchmark environments have recently propelled AI 2. Real-World and Virtual Team Sports progress and led to a huge impact in the media, thereby con- fi “ ” tributing to the current AI hype. There is no universal de nition of team sports. According “ The game of Go was already an unofficial AI benchmark to Collins Dictionary, a team sport is a sport in which teams ” when chess lost much of its appeal for AI researchers as a play against each other [11]. The current Wikipedia article fi “ result of DeepBlue’s victory over Kasparov in 1997. Whereas expands this de nition as follows: A team sport includes Silver et al. [4] already provided an approach that was able to any sport where individuals are organized into opposing beat professional human Go players, the old Atari console teams which compete to win. Team members act together environment was used to invent Deep Reinforcement Learn- towards a shared objective. This can be done in a number ” ing (DRL) [5, 6]. It soon turned out that most of the games of ways such as outscoring the opposing team [12]. of the so-called Atari learning environment (ALE) are rela- For some sports, one may wonder to which extent they fl tively easy to deal with by this new technique; even playing should be considered full- edged team sports. For example, in many games with superhuman performance was quickly rowing, relay racing, or swimming, there are competing teams achieved. In search for new challenges, research turned to of athletes, but little communication is required among team real-time strategy (RTS) games. members and virtually no contact with the opposing team. StarCraft was established as a benchmark and competi- More interesting cases are sports in which teams take on ff ff tion environment in 2010 [7] and was still considered very distinct roles and follow di erent objectives in di erent fi challenging when DeepMind and Blizzard teamed up to pro- phases of the game, like elding team and batting team in vide the SC2LE environment for StarCraft 2 in 2017 [8]. Fol- baseball, or when each team consists of several groups with lowing the reports of the AlphaStar team of DeepMind [9], it immutable roles as in quidditch (https://en.wikipedia.org/ seems that RTS games are not yet completely done, but get- wiki/Quidditch_(sport)), where two largely independent ting in reach. This is surprising on one hand, because most but somehow interacting games (chaser and beater) take fi researchers had expected it to take years to get that far. On place on the same eld, extended by a third one (seeker) the other hand, there is higher emphasis on generalizability during the end phase of the game. now: we are not satisfied with an AI system that can be used Having noted these scenarios, let us narrow down the for a single game and in previously seen environments any- subsequent discussion to the types of sports in which both more; we expect the AI shall be able to transfer knowledge teams have the same structure and follow the same goal as, between situations, environments, and eventually games. for example, in ice hockey, basketball, soccer, and volleyball. An interesting development in this direction is the Obsta- Sport games are also a genre staple of the video game cle Tower Challenge [10] that features almost endless gener- industry. Since Tennis for Two (1958), virtual renditions of ated mazes that gradually get more difficult. Generalization sport games spread literally to every gaming platform avail- in game mechanisms and visuals is key here, but there is only able. Obviously, it is not possible to capture all aspects of a ff a single avatar that is completely autonomous; thus, interac- physical athletic activity on a computer, and di erent game ff tion happens only with more or less static objects. projects emphasize di erent elements of real-world sport Another aspect, the team play between different AI sys- events. As extreme examples, departing from a somewhat tems, is emphasized in the cooperating OpenAI Five standard formula of a sport video game, we can mention Sub- (http://openai.com/blog/openai-five-benchmark-results/) buteo (1990), representing soccer as a turn-based billiard-like Multiplayer online battle arena (MOBA) game bots. The strategy, Captain Tsubasa (1988), emphasizing role-playing number of bots in a team is limited to five, but they clearly elements of soccer and player relationships, and a number need to work together well in order to win a game—a capabil- of games like Super Mario Strikers (2005) or Nintendo World “ ” ity that is also fundamental for team sport AI. We, therefore, Cup (1990), with unrealistic fun elements such as powerful ’ also compare the properties of MOBA, RTS, and team sport supershots, players special abilities, and extreme weather games and the resulting challenges for AI later on. conditions. Even within the same franchise, developers “ ” With this work, we highlight the need to define good bench- sometimes create games that emphasize simulation or “ ” ’ mark environments that integrate team AI, here understood as fun aspects, such as in case of EA s NBA Live vs. NBA multiple game AIs that interact among themselves and with Jam or NHL vs. NHL Slapshot. Therefore, the discussion “ ” fi humans in order to reach a common goal. For this, team sport around team sports as a game AI testbed requires rst games provide an excellent context with numerous advantages.
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