Intelligent prompting systems for people with cognitive disabilities

Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 2014.The considerable impact that intelligent prompting systems could have on the lives of people with cognitive disabilities is gaining wide interest. Such intelligent systems would be able to support a variety of cognitive tasks by quickly recognizing different situations, performing autonomous decision making, and taking appropriate actions. While an intelligent prompting system offers significant benefits, it also proves to be an extremely challenging design paradigm. A successful prompting system that meets the needs of practical use requires the ability to trade off conflicting objectives, make decisions in the face of all kinds of uncertainties, and support a wide range of prompting behaviors that can adapt to diverse user needs and preferences. To handle all these complexities in the prompting domain, researchers have been studying the use of POMDPs (partially observable Markov decision processes) to formulate the problem in a well-formed theoretic framework. However, solving a POMDP problem in a large domain is quite challenging and even intractable. In this dissertation, we are addressing this challenge by proposing a hierarchical POMDP model that runs on a set of individual option-based MDPs (Markov decision processes). Options are temporal extended actions that can facilitate learning by exploiting the problem structure and supporting the specification of a variety of complex prompting routines. This approach is then combined with a dual control heuristic to convert a solution of the underlying completely observable control algorithm to the POMDP’s. To further enhance the system’s performance, we have developed several novel techniques that work in ways complementary to each other. These include (i) adaptive options that optimize the timing of the prompt and quickly adapt to different user behaviors, the performance of which is shown based on both simulation and human subject experiments; (ii) a selective-inquiry based dual control algorithm that only asks questions of the user when necessary; (iii) robust state estimation that recovers a history of events as well as their timings, and handles interruptions and resumptions in the task execution. Based on these, we propose a unified control model and demonstrate its outstanding performance by comparing it with alternative models. Finally, to further test the performance of the unified model, we implemented it as a prototype system and ran it with human subjects through exemplified scenarios in a controlled lab environment