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An important application of interactive machine learning is extending oramplifying the cognitive and physical capabilities of a human. To accomplishthis, machines need to learn about their human users' intentions and adapt totheir preferences. In most current research, a user has conveyed preferences toa machine using explicit corrective or instructive feedback; explicit feedbackimposes a cognitive load on the user and is expensive in terms of human effort.The primary objective of the current work is to demonstrate that a learningagent can reduce the amount of explicit feedback required for adapting to theuser's preferences pertaining to a task by learning to perceive a value of itsbehavior from the human user, particularly from the user's facialexpressions---we call this face valuing. We empirically evaluate face valuingon a grip selection task. Our preliminary results suggest that an agent canquickly adapt to a user's changing preferences with minimal explicit feedbackby learning a value function that maps facial features extracted from a cameraimage to expected future reward. We believe that an agent learning to perceivea value from the body language of its human user is complementary to existinginteractive machine learning approaches and will help in creating successfulhuman-machine interactive applications.

Face valuing: Training user interfaces with facial expressions and reinforcement learning