A Decision-Theoretic Framework for Assistive Technologies

Abstract

The potential of assistive technologies to transform the lives of both able and disabled people cannot be overestimated. In this talk, I describe a decision-theoretic framework that captures the general problem of optimally assisting a goal-directed user. Since the goals of the users are typically unobserved, a key problem is to infer them from their actions, and balance the uncertainty of the goal with the usefulness of the help offered. We study several instances of this problem as special cases of more general Partially observable Markov Decision Processes (POMDPs). We apply this framework to a number of domains including the real-world task of folder prediction in Windows, and show that, in spite of the bad worst-case complexity, the performance of myopic heuristics is quite good. We develop a formal model that explains the effectiveness of the myopic heuristics and derive a simple bound on the worst case number of mistakes made relative to an assistant who knows the goals of the user. We suggest open problems and future directions in this line of research to advance the state of the art in assistive technologies.

Joint work with Alan Fern, Sriraam Natarajan, and Kshitij Judah.