Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An important application of interactive machine learning is extending or amplifying the cognitive and physical capabilities of a human. To accomplish this, machines need to learn about their human users' intentions and adapt to their preferences. In most current research, a user has conveyed preferences to a machine using explicit corrective or instructive feedback; explicit feedback imposes 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 learning agent can reduce the amount of explicit feedback required for adapting to the user's preferences pertaining to a task by learning to perceive a value of its behavior from the human user, particularly from the user's facial expressions--- call this face valuing.  empirically evaluate face valuing on a grip selection task. Our preliminary results suggest that an agent can quickly adapt to a user's changing preferences with minimal explicit feedback by learning a value function that maps facial features extracted from a camera image to expected future reward.  believe that an agent learning to perceive a value from the body language of its human user is complementary to existing interactive machine learning approaches and will help in creating successful human-machine interactive applications.
['Vivek Veeriah', 'Patrick M. Pilarski', 'Richard S. Sutton']
I am writing a research paper on a idea Face valuing: Training user interfaces with facial expressions and reinforcement learning
[{"from": "human", "value": "I am writing a research paper on a idea Face valuing: Training user interfaces with facial expressions and reinforcement learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An important application of interactive machine learning is extending or amplifying the cognitive and physical capabilities of a human. To accomplish this, machines need to learn about their human users' intentions and adapt to their preferences. In most current research, a user has conveyed preferences to a machine using explicit corrective or instructive feedback; explicit feedback imposes 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 learning agent can reduce the amount of explicit feedback required for adapting to the user's preferences pertaining to a task by learning to perceive a value of its behavior from the human user, particularly from the user's facial expressions--- call this face valuing.  empirically evaluate face valuing on a grip selection task. Our preliminary results suggest that an agent can quickly adapt to a user's changing preferences with minimal explicit feedback by learning a value function that maps facial features extracted from a camera image to expected future reward.  believe that an agent learning to perceive a value from the body language of its human user is complementary to existing interactive machine learning approaches and will help in creating successful human-machine interactive applications."}]