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Department of Computer Science Colloquium
2007 Series

A Decision-Theoretic Approach to Task Assistance for Persons with Dementia

Dr. Pascal Poupart
University of Waterloo

Date: January 10, 2007 (Wednesday)
Time: 3:00 - 4:00 pm
Venue: SCT909, Cha Chi Ming Science Tower, Ho Sin Hang Campus

People suffering from dementia (e.g., Alzheimer's disease) often have difficulty completing even simple activities of daily living (ADL) such as toileting, dressing, eating, taking medication, etc. Cognitive assistive technologies hold the promise to provide people suffering from dementia with an increased level of independence. In this talk, I will first describe a system that guides patients with memory deficiencies through the steps of a simple task: handwashing. The system monitors patient progress in the task of handwashing with video-cameras and when necessary, prompts the next step with a verbal cue. The talk will focus on how to design effective prompting strategies that take into account the uncertainty due to the inherent noise of the video sequence as well as the fact that patients do not always follow the prompts. More specifically, I will explain how to model robust prompting strategies with partially observable Markov decision processes. In a second part of my talk, I will present a new technique for scaling POMDP optimization algorithms in domains with continuous observations spaces such as assistive technologies. The rich observation spaces often produced by sensing devices such as video-cameras, microphones or sonars pose significant problems for standard POMDP algorithms that require explicit enumeration of the observations. This problem is usually approached by imposing an a priori discretisation of the observation space, which can be sub-optimal for the decision making task. However, since only those observations that would change the course of action need to be distinguished, the planning task induces a lossless partitioning of the observation space. In this talk, I will explain how to find this partition, and how the resulting discretisation reveals the relevant observation features of the application domain. This will be demonstrated with the handwashing guidance task presented in the first part of the talk.

Pascal Poupart is an Assistant Professor in the David R. Cheriton School of Computer Sicence at the University of Waterloo in Waterloo, Canada. He received the Ph.D. in Computer Science (2005) from the University of Toronto in Toronto, Canada, the M.Sc. in Computer Science (2000) from the University of British Columbia in Vancouver, Canada, and the B.Sc. in Mathematics and Computer Science (1998) from McGill University in Montreal, Canada. His research focuses on the development of decision-theoretic planning and machine learning teachniques for autonomous and adaptive systems in various domains, including assistive technologies for elder care, spoken dialog systems, natural language processing and preference elicitation. He recently served on the program committee of several international conferences, including IJCAI, AAAI, UAI, ICML, AAMAS, EMNLP and co-organized the AAAI-06 workshop on Empirical and Statistical Methods for Spoken Dialogue Systems.

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