Identifying Symbolic Communication in a Simulated Teacher-Student Environment

Symbolic communication is an inherent and intuitive aspect of the human experience. In this project, we propose, implement, and run inference on a probabilistic Bayesian model for identifying symbolic communication. We focused on a recently proposed simulated teacher-student environment where we have access to human data. We show several qualitative and quantitative results that compare our model with human judgments. These results suggest that our approach is reasonably effective at identifying symbolic communication with adequate accuracy. We utilize the Gen probabilistic programming framework for the implementation of our model.