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AIC Seminar Series

Partially Observable Markov Decision Processes for Human-Interactive Tasks

Jesse HoeyUniversity of Toronto

Date:  2006-01-10 at 10:00

Location:  EJ228  (Directions)

   Abstract

Partially observable Markov decision processes (POMDPs) are a stochastic model of an agent interacting with an environment. POMDPs have long been known in operations research as an efficient and principled way of modeling a temporal process of action in an uncertain environment. POMDPs have also gained popularity as a model in artificial intelligence. Unfortunately, POMDPs have traditionally been viewed as an intractable solution to real-world problems. However, recent research has demonstrated scalable approximate solution techniques for POMDPs. In this talk, I will describe some recent work on using POMDPs for modeling large-scale real world decision problems. In particular, I will discuss using POMDPs to model tasks in which a human interacts with an autonomous system that senses its environment using only cameras. This problem is challenging for two reasons. First, visual observations (e.g. from a camera) are very rich in information, but are very noisy. Second, video and decision making occur on very different time scales, requiring hierarchical models. I will discuss a fairly general approach to this problem, and then investigate in more detail some of the more technical aspects. I will show examples (both algorithmic and empirical) from a system that helps persons with Alzheimer’s disease accomplish activities of daily living (ADL).

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