Challenges and Opportunities for Reinforcement Learning in Human Computer Interaction Systems

Satinder Singh

Computer Science and Engineering Division

University of Michigan, Ann Arbor

 

Many human-computer interaction (HCI) systems are sequential interaction systems in which the designer has incomplete and uncertain knowledge about the system's environment and in which user-feedback is impoverished, noisy, and delayed in time. These are precisely the sort of problems reinforcement learning (RL) methods are good at solving. In this talk, I will discuss the opportunities and challenges facing the use of RL as a rigorous design principle for HCI, and illustrate my arguments using examples from 3 simple RL-based HCI systems that I have helped build: an adaptive spoken-dialogue system, an interactive software agent in an online community, and most recently an adaptive reminder system in a cognitive orthotic domain.

 

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