Applying Probabilistic Programming to Affective Computing
We propose a probabilistic programming approach to affective computing.
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We propose a probabilistic programming approach to affective computing.
We present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions.
Harold was awarded the faculty teaching award for the year 18/19!
We show that iCub robot classifies the surface textures with both sliding and touch movements under loose constraints with high accuracy.
Sreejith Balakrishnan is awarded the 2019 Honor List of Student Tutors for his contribution as a tutor to the module “CS4246: AI Planning and Decision Making”.
Using Bayesian Optimization to address the ill-posed nature of Inverse Reinforcement Learning
Harold was awarded the faculty teaching award for the year 17/18! A first time win.