Structure Learning on Large Scale Common Sense Statistical Models of Human State Advisor: Hsin-His Chen Reporter: Chi-Hsin Yu From AAAI 2008 William Pentney, Department of Computer Science & Engineering University of Washington Matthai Philipose, Intel Research Seattle Jeff Bilmes, Department of Electrical Engineering University of Washington
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Advisor: Hsin-His Chen Reporter: Chi-Hsin Yu Date: 2009.02.11 From AAAI 2008 William Pentney, Department of Computer Science & Engineering University of.
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William Pentney, Department of Computer Science & Engineering University of Washington
Matthai Philipose, Intel Research Seattle
Jeff Bilmes, Department of Electrical Engineering University of Washington
Common Sense Data Acquisition for Indoor Mobile Robots◦ AAAI 2004◦ Rakesh Gupta and Mykel J. Kochenderfer
Sensor-Based Understanding of Daily Life via Large-Scale Use of Common Sense ◦ AAAI 2006◦ William Pentney, et al., Matthai Philipose (Intel)
Learning Large Scale Common Sense Models of Everyday Life ◦ AAAI 2007◦ William Pentney, et al., Matthai Philipose (Intel)
Related Papers
Introduction Data Acquisition and Representation Inference Evaluation Methodology and Results Conclusion
Outlines
Common sense◦ being critical to the automated understanding of
the world (Example)◦ OMICS (Open Mind Indoor Common Sense) project
This paper◦ enabling correspondingly large scale sensor-based
understanding of the world (RFID)
Introduction
Challenges◦ semantic gaps (facts in DB - phenomena detected by
sensors)◦ fragility of reasoning in the face of noise ◦ Incompleteness of repositories (DB) ◦ slowness of reasoning with these large repositories
The adaptation of using sensor data is challenging because it is unclear that …◦ how to represent models◦ term occurrence statistics are a practical means of
acquiring arbitrary common sense information from the web
Introduction (Cont.)
Collecting common sense data through the Open Mind Indoor Common Sense (OMICS) website
Restricting the domain to indoor home and office environments
Data Acquisition – OMICS(AAAI 2004)
Data Acquisition – OMICS (Cont.)
Hand proximity to objects implies object use. Three users perform various daily activities.
A total of 5-7 minutes of each activity was collected, for a total of 70-75min of data.
These traces were divided into time slices of 2.5s.
Data Acquisition – Sensor Data
Data Acquisition and Rep. - Flow
SRCS=State Recognition using Common Sense
Template to relation ◦ “You <eat> when you are <hungry>.” ◦ Relation: people(<Action>, <Context>)◦ Ex:
Relation People people (‘eat’, ‘hungry’) people(’drink water’,’are thirsty’)