Title
Paying Attention to What Matters: Observation Abstraction in Partially Observable Environments
Date of Award
2-2010
Document type
dissertation
Access Type
Open Access Dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Computer Science
First Advisor
Andrew G. Barto
Second Advisor
Sridhar Mahadevan
Third Advisor
Shlomo Zilberstein
Subject Categories
Computer Sciences
Abstract
Autonomous agents may not have access to complete information about the state of the environment. For example, a robot soccer player may only be able to estimate the locations of other players not in the scope of its sensors. However, even though all the information needed for ideal decision making cannot be sensed, all that is sensed is usually not needed. The noise and motion of spectators, for example, can be ignored in order to focus on the game field. Standard formulations do not consider this situation, assuming that all the can be sensed must be included in any useful abstraction. This dissertation extends the Markov Decision Process Homomorphism framework (Ravindran, 2004) to partially observable domains, focusing specically on reducing Partially Observable Markov Decision Processes (POMDPs) when the model is known. This involves ignoring aspects of the observation function which are irrelevant to a particular task. Abstraction is particularly important in partially observable domains, as it enables the formation of a smaller domain model and thus more efficient use of the observed features.
DOI
https://doi.org/10.7275/1266281
Recommended Citation
Wolfe, Alicia Peregrin, "Paying Attention to What Matters: Observation Abstraction in Partially Observable Environments" (2010). Open Access Dissertations. 188.
https://doi.org/10.7275/1266281
https://scholarworks.umass.edu/open_access_dissertations/188