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.
Highlights
Artificial intelligence (AI) researchers have often relied on popular games as testbeds for evaluating new algorithms and approaches
We can say that most team sports have similar features: they are popular among the general public, they are easy to set up, and they require the participating team members to exhibit both athletic abilities and a certain level of tactical and strategic thinking
After discussing the interplay of strong and fun game artificial intelligence (AI) in Section 5, we look at the tactics and strategy perspective (Section 6), before more generally collecting the challenges of sports AI, in comparison with Multiplayer online battle arena (MOBA) and real-time strategy (RTS) (Section 7) and ending with conclusions
Summary
Artificial intelligence (AI) researchers have often relied on popular games as testbeds for evaluating new algorithms and approaches. If we add communication to the team sport definition above, we may get a more complete definition: team sports are games that involve at least two teams playing against each other, each composed of a set of players with their individual roles and abilities who cooperate by means of communication in order to win the game These features make team sports an interesting challenge for a game AI system, and we, propose to consider them as a promising testbed for AI benchmarking. Generalization in game mechanisms and visuals is key here, but there is only a single avatar that is completely autonomous; interaction happens only with more or less static objects Another aspect, the team play between different AI systems, is emphasized in the cooperating OpenAI Five (http://openai.com/blog/openai-five-benchmark-results/) Multiplayer online battle arena (MOBA) game bots. After discussing the interplay of strong and fun game AI in Section 5, we look at the tactics and strategy perspective (Section 6), before more generally collecting the challenges of sports AI, in comparison with MOBA and RTS (Section 7) and ending with conclusions
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