Game AI Competitions: Motivation for the Imitation Game-Playing Competition

Świechowski, Maciej

doi:10.15439/2020f126

Cited by 11 publications

(7 citation statements)

References 46 publications

(39 reference statements)

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“…The purpose of Game AI (...) is to support that experience." Thus, depending on a particular game, the goal of a good AI system might well involve being strong, predictable, erratic, friendly, hostile, and so on [29]. In other words, a good game AI testbed should support, at least, theoretically, various possible goals for AI-controlled characters.…”

Section: Strong Game Ai and Fun Game Aimentioning

confidence: 99%

Team Sports for Game AI Benchmarking Revisited

Mozgovoy

Preuß

Bidarra

2021

International Journal of Computer Games Technology

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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.

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Section: Strong Game Ai and Fun Game Aimentioning

confidence: 99%

Team Sports for Game AI Benchmarking Revisited

Mozgovoy

Preuß

Bidarra

2021

International Journal of Computer Games Technology

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“…those in medical field. Game studios extensively test the AI in their games to minimize the risk of players encountering unexpected or unnatural behavior in the shipped game [28].…”

Section: Explainability In Gamesmentioning

confidence: 99%

Introducing LogDL - Log Description Language for Insights from Complex Data

Ślęzak

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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We propose a new logic-based language called Log Description Language (LogDL), designed to be a medium for the knowledge discovery workflows over complex data sets. It makes it possible to operate with the original data along with machinelearning-driven insights expressed as facts and rules, regarded as so-called descriptive logs characterizing the observed processes in real or virtual environments. LogDL is inspired by the research at the border of AI and games, precisely by Game Description Language (GDL) that was developed for General Game Playing (GGP). We emphasize that such formal frameworks for analyzing the gameplay data are a good prerequisite for the case of real, "not digital" processes. We also refer to Fogs of War (FoW)our upcoming project related to AI in video games with limited information -whereby LogDL will be used as well.

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“…Many problems encountered in games reflect real-world problems, which made the game's uses much more impactful [5]. Based on such notions, research in games had branched into many sub-fields, encompassing many disciplines.…”

Section: Introductionmentioning

confidence: 99%

Computing Games: Bridging the Gap Between Search and Entertainment

2021

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Over history, games have served multiple purposes. It serves as a fun activity for players who need the entertainment to become test-beds for artificial intelligence. Solving games is beneficial in providing a better understanding of how information is progressing throughout the game. Uncertainty in games affects the way a game is solved, and the way the game is experienced. Previous works have interpreted uncertainty in the game progress through various means, but there have been no clear links among those interpretations. In this paper, the probability-based proof number search (PPNS) and single conspiracy number (SCN) were used as the domain-independent indicators to analyze how uncertainty affects various game elements. PPNS exploits information from certain and uncertain information to reach convergence in solving games. Meanwhile, SCN evaluates the game states' difficulty and describes game-playing patterns to understand play positions better. Experiments results demonstrate the link between the search indicators and the measure of entertainment where uncertainty plays a vital role in both contexts, verified from both twoperson and single-agent games. Such a situation is also crucial for both computation and entertainment measures since it impacts both the quality of information and the expected game-playing experience.

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Game AI Competitions: Motivation for the Imitation Game-Playing Competition

Cited by 11 publications

References 46 publications

Team Sports for Game AI Benchmarking Revisited

Team Sports for Game AI Benchmarking Revisited

Introducing LogDL - Log Description Language for Insights from Complex Data

Computing Games: Bridging the Gap Between Search and Entertainment

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