As Artificial Intelligence systems are used by human users at an increasing frequency, the need for such systems to understand and predict human behavior likewise increases. In my work, I have considered how to predict human behavior in repeated games. These repeated games can be applied as a foundation to many situations where a person may interact with an AI, In an attempt to create such a foundation, I have built a system using Attitude Vectors used in automata to predict actions based on prior actions and communications. These Attitude Vector Automata (AVA) can transform information from actions in one game with a given payoff matrix into actions in another game. Results show that prediction accuracy was ultimately below other, similar work, in general in several repeated games. There are however some aspects, such as scenarios involving lying, in which my predictor showed potential to outperform these other systems. Ultimately, there is potential in using ideas presented as AVA to build a potentially more robust system for future efforts in human behavior prediction.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
James, Brian L., "Predicting Human Behavior in Repeated Games with Attitude Vectors" (2021). Theses and Dissertations. 9239.
generalizing, human machine interaction, artificial intelligence, S#, Attitude Vector, Attitude Vector Automata, AVA