Studying human spatial navigation processes using POMDPs

Brian J. Stankiewicz, Matthew McCabe, Gordon E. Legge

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Humans possess the remarkable ability to navigate through large-scale spaces, such as a building or a city, with remarkable ease and proficiency. The current series of studies uses uses Partially Observable Markov Decision Processes (POMDP) to better understand how humans navigate through large-scale spaces when they have state uncertainty (i.e., lost in a familiar environment.). To investigate this question, we familiarized subjects with a novel, indoor, virtual reality environment. After familiarizing the subject with the environment, we measured subject's efficiency for navigating from an unspecified location within the environment to a specific goal state. The environments were visually sparse and thus produced a great deal of perceptual aliasing (more than one state produced the same observation). We investigated whether human inefficiency was due to: 1) accessing their cognitive map; 2) Updating their belief vector; or 3) An inefficient decision process. The data clearly show that subjects are limited by an inefficient belief vector updating procedure. We discuss the ramifications of these finding on human way-finding behavior in addition to more general issues associated with decision making with uncertainty.

Original languageEnglish (US)
Pages97-102
Number of pages6
StatePublished - Dec 1 2004
Event19th National Conference on Artificial Intelligence - San Jose, CA, United States
Duration: Jul 25 2004Jul 26 2004

Other

Other19th National Conference on Artificial Intelligence
CountryUnited States
CitySan Jose, CA
Period7/25/047/26/04

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