Applications of Machine Learning in Computer Games

Dear Colleagues,

Artificial intelligence (AI) and, more precisely, machine learning can aid the multibillion-dollar gaming industry in terms of both game development and gameplay. In the 1950s, Alan Turing pointed out that computer games could aid the development of intelligent machines. AI has evolved into a central part of computer games, from controlling chess pieces to simulating realistic-looking virtual worlds. Traditional AI with pre-defined rules is capable of solving problems in a deterministic environment. Playing a chess game against the computer at a high difficulty can be very challenging even for the most experienced players. However, with the introduction of open worlds and systemic gaming, there is a high level of entropy, which limits the use of pre-defined AI.

In modern games, machine learning finds its rightful place in a number of areas. It can be used for modeling the behavior of virtual life, such as virtual characters, animals and other virtual objects, as well as their interactions within the virtual world. All these virtual entities can be procedurally generated based on various objectives and constraints. The interaction between the players and the virtual world can be enhanced by the use of AI and machine learning and make the player’s in-game decision have a real impact on the world and the story. Live operations (Live-ops) can also benefit from these approaches, as from the auto-generation of seasonal events, special in-game entities and other live events. Apart from enhancing the user’s in-game experience, machine learning techniques can be used at a monetarization level for auto-generating personalized in-game offers, auto-determining the prices of virtual goods, and predicting trends.

This call aims to explore the intersection of computer games and machine learning. The topics of interest include but are not limited to:

• The behavior modeling/simulation of virtual entities;
• Procedural generations;
• Cognitive path finding;
• Competitive AI;
• Applications of reinforcement learning in gaming;
• User behavior modeling and prediction;
• The prediction of trends;
• Dynamic monetarization;
• Games and education;
• Intelligent gaming hardware.

Dr. Milos Stojmenovic
Dr. Georgios Tsaramirsis
Guest Editors

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• gaming
• gaming and education
• gaming artificial intelligence
• applied machine learning and gaming
• gamification
• intelligent non playable characters (NPCs)
• procedural generations
• gaming and digital twins
• simulations