Abstract by Najma Mathema

Personal Infomation

Presenter's Name

Najma Mathema

Degree Level


Abstract Infomation


Computer Science

Faculty Advisor

Mike Goodrich


Modeling Human Behavior in Repeated Games


Modeling other agents is important for longitudinal Human-Robot Interaction (HRI). Our work improves HRI by forming a model of belief and behavior of humans interacting with algorithms. Our work builds on prior work on repeated games with cheap talk, which enables HRI and human-human interaction. A Bayes Filter has been implemented for modeling human behaviors in repeated games. The filter predicts the next actions for the modeled agent. The overall goal is to enable a relationship narrative of the agents’ interaction, which would improve long-term HRI. On the Prisoners Dilemma, the filter accurately predicted 91.2% of next actions when modeling S# algorithm(robot), and 88.9% when modeling humans. Future work will use the Viterbi algorithm to suggest the most favorable actions for modeling long-term interactions.