Research Topic

Artificial Intelligence and Big Data Analytics in Games’ Experience

About this Research Topic

Artificial intelligence (AI) in games is a concept as old as artificial intelligence itself. One of the first ever AI algorithms in the 1950s was designed to simulate the board games of checkers. Since then, AI algorithms have been linked to many applications related to computer games. These applications might use AI algorithms to augment the behavior of non-player characters as well as the design of games’ environments that are informed by the players’ responses and the style of the play. They might additionally use AI algorithms as a test bed for the development of new algorithms for general AI. With strict rules and rewards systems, game worlds are a particularly useful environment for training software. The intention is that by teaching this software to play games, human researchers can understand how to train machines to perform more complicated tasks in the future.

Recent developments in AI such as deep learning can make non-player characters using machine learning algorithms nearly invincible. In this respect the use of advanced AI algorithms in modelling intelligent non-player characters and impenetrable environments does not necessarily improve the experience of the players. However, there is a place in the development of games where deep learning AI can be used to create new and fulfilling game experiences.

Advances in deep learning known as generative adversarial networks (GANs), use a pair of deep neural networks to try to accurately replicate patterns that are indistinguishable from the originals. Examples of GANs’ applications in games include the creation of non-player characters with lifelike facial, voice, and motion characteristics. They equally include the production of realistic environments and the generation of levels, and, more recently, the invention of new games. In other areas, such as affective computing in games, applications of AI algorithms are concerned with the seamless detection of a user’s emotional state or reaction to a game through on-line facial or behavioral recognition. Even more so, researchers recently advocated that bots could learn the best winning strategies by collecting relevant data. In so doing, they could offer personalized suggestions based on a user’s gameplay. Building on such data collection and other mined information game developers could gain a deep understanding of users’ behaviors. As a result, they could advance games to provide players with improved and unique experiences.

Building on the above, the aim of this Research Topic is to explore the potential applications of advanced AI algorithms to enhance players game experience. We welcome contributions in the broad areas of:

• Procedural generation of environments
• lifelike non-player characters
• affective computing in games
• analytics and player modelling through data mining
• team playing
• advances in games testing


Keywords: artificial intelligence, PCG, lifelike NPC, deep learning, affective computing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Artificial intelligence (AI) in games is a concept as old as artificial intelligence itself. One of the first ever AI algorithms in the 1950s was designed to simulate the board games of checkers. Since then, AI algorithms have been linked to many applications related to computer games. These applications might use AI algorithms to augment the behavior of non-player characters as well as the design of games’ environments that are informed by the players’ responses and the style of the play. They might additionally use AI algorithms as a test bed for the development of new algorithms for general AI. With strict rules and rewards systems, game worlds are a particularly useful environment for training software. The intention is that by teaching this software to play games, human researchers can understand how to train machines to perform more complicated tasks in the future.

Recent developments in AI such as deep learning can make non-player characters using machine learning algorithms nearly invincible. In this respect the use of advanced AI algorithms in modelling intelligent non-player characters and impenetrable environments does not necessarily improve the experience of the players. However, there is a place in the development of games where deep learning AI can be used to create new and fulfilling game experiences.

Advances in deep learning known as generative adversarial networks (GANs), use a pair of deep neural networks to try to accurately replicate patterns that are indistinguishable from the originals. Examples of GANs’ applications in games include the creation of non-player characters with lifelike facial, voice, and motion characteristics. They equally include the production of realistic environments and the generation of levels, and, more recently, the invention of new games. In other areas, such as affective computing in games, applications of AI algorithms are concerned with the seamless detection of a user’s emotional state or reaction to a game through on-line facial or behavioral recognition. Even more so, researchers recently advocated that bots could learn the best winning strategies by collecting relevant data. In so doing, they could offer personalized suggestions based on a user’s gameplay. Building on such data collection and other mined information game developers could gain a deep understanding of users’ behaviors. As a result, they could advance games to provide players with improved and unique experiences.

Building on the above, the aim of this Research Topic is to explore the potential applications of advanced AI algorithms to enhance players game experience. We welcome contributions in the broad areas of:

• Procedural generation of environments
• lifelike non-player characters
• affective computing in games
• analytics and player modelling through data mining
• team playing
• advances in games testing


Keywords: artificial intelligence, PCG, lifelike NPC, deep learning, affective computing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

22 January 2021 Manuscript
15 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

22 January 2021 Manuscript
15 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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