Enhancing Argument Generation Using Bayesian Networks

In this paper, we examine algorithms that utilize factor graphs from Bayesian Belief Networks to generate and evaluate arguments. We assess their strengths and weaknesses, which leads to the creation of our improved algorithm that rectifies the issues that we identified. Our approach includes applying the original and modified algorithms to previously known networks to pose challenges in generating robust arguments for humans and computers. Our findings reveal significant improvements in the creation of more robust arguments. Moreover, we delve into the dynamics of argument interaction, offering detailed insight into the algorithms' practical efficacy.