TY - GEN
T1 - Q-Learning with Naïve Bayes Approach Towards More Engaging Game Agents
AU - Yilmaz, Osman
AU - Celikcan, Ufuk
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - One of the goals of modern game programming is adapting the life-like characteristics and concepts into games. This approach is adopted to offer game agents that exhibit more engaging behavior. Methods that prioritize reward maximization cause the game agent to go into same patterns and lead to repetitive gaming experience, as well as reduced playability. In order to prevent such repetitive patterns, we explore a behavior algorithm based on Q-learning with a Naïve Bayes approach. The algorithm is validated in a formal user study in contrast to a benchmark. The results of the study demonstrate that the algorithm outperforms the benchmark and the game agent becomes more engaging as the amount of gameplay data, from which the algorithm learns, increases.
AB - One of the goals of modern game programming is adapting the life-like characteristics and concepts into games. This approach is adopted to offer game agents that exhibit more engaging behavior. Methods that prioritize reward maximization cause the game agent to go into same patterns and lead to repetitive gaming experience, as well as reduced playability. In order to prevent such repetitive patterns, we explore a behavior algorithm based on Q-learning with a Naïve Bayes approach. The algorithm is validated in a formal user study in contrast to a benchmark. The results of the study demonstrate that the algorithm outperforms the benchmark and the game agent becomes more engaging as the amount of gameplay data, from which the algorithm learns, increases.
KW - Engaging Gameplay
KW - Game AI
KW - Naïve Bayes
KW - Q-Learning
KW - Reinforcement Learning
UR - https://www.scopus.com/pages/publications/85062514613
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=performanshacettepe&SrcAuth=WosAPI&KeyUT=WOS:000458717400174&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1109/IDAP.2018.8620897
DO - 10.1109/IDAP.2018.8620897
M3 - Conference contribution
AN - SCOPUS:85062514613
T3 - 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018
BT - 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018
Y2 - 28 September 2018 through 30 September 2018
ER -