Intent inference and action prediction using a Computational Cognitive Model

This paper proposes a Computational Cognitive Model (CCM) inspired by the biological mirror neurons and the theory of mind reading for high level information fusion, in particular, intent inference and action prediction. Existing computational prediction models in the literature would usually build the other person's mental model from scratch; however, this would not work if there is no knowledge about the other person initially. Instead, this paper uses one's own mental model at the start for prediction of the other person and does a perspective changing (or mirroring), i.e., putting oneself in the other person's shoes, to infer the other's thoughts and actions. A model analysis using simulated data is carried out and results show that by using mirroring principles, convergence to the other person's mental model is faster than using baseline method (prediction with an equal probability distribution), and it is also able to give higher prediction accuracy. In addition, feedback and updating mechanisms in the proposed model help to ensure convergence towards the other person's mental model.