Computational Media Design: Using Graph Data to Improve Non-Player Character Acting in Games
Date
2023-09-14
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Abstract
This thesis uses graph databases to enhance non-player character (NPC) behavior in computer games. The approach is tested in three discrete projects by developing and using the Neo4jConnector, a custom toolkit enabling novel bi-directional communication between real-time simulation data and server-based graph database long-term storage. The first project demonstrates the Neo4jConnector’s use in recording player movement as graph data and facilitating NPC movement playback from this data. The second project explores improving NPCs’ non-deterministic behaviors through q-learning, a reinforcement learning algorithm, and storing the resultant graph data in the server-based database. The third project investigates dynamically loading 3D geometry from a graph database in multi-scale cellular simulations, supporting intricate bioinformatics simulations necessitating non-deterministic agent actions. Emphasizing an artist’s approach to Computational Media Design, the research targets animators, game makers, bioinformatics researchers, and the growing field of games research. Through an elaborative design process, the thesis showcases the value of incorporating graph databases in game production environments, offering new opportunities to create more realistic and adaptive NPC behaviors that boost player engagement and improve the quality of simulations for future research.
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Keywords
Computer Game Production, Graph Databases, Non-Player Character Acting, Reinforcement Learning
Citation
Brierley, O. D. (2023). Computational Media Design: using graph data to improve non-player character acting in games (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.