text-only page produced automatically by Usablenet Assistive Skip all navigation and go to page content Skip top navigation and go to directorate navigation Skip top navigation and go to page navigation
National Science Foundation
Awards
design element
Search Awards
Recent Awards
Presidential and Honorary Awards
About Awards
Grant Policy Manual
Grant General Conditions
Cooperative Agreement Conditions
Special Conditions
Federal Demonstration Partnership
Policy Office Website



Award Abstract #0953723

CAREER: Explanation, Decision Making, and Learning in Graphical Models

NSF Org: IIS
Div Of Information & Intelligent Systems
divider line
Initial Amendment Date: March 25, 2010
divider line
Latest Amendment Date: December 13, 2011
divider line
Award Number: 0953723
divider line
Award Instrument: Standard Grant
divider line
Program Manager: Hector Munoz-Avila
IIS Div Of Information & Intelligent Systems
CSE Direct For Computer & Info Scie & Enginr
divider line
Start Date: August 15, 2010
divider line
End Date: July 31, 2016 (Estimated)
divider line
Awarded Amount to Date: $480,233.00
divider line
Investigator(s): Changhe Yuan changhe.yuan@qc.cuny.edu (Principal Investigator)
divider line
Sponsor: Mississippi State University
PO Box 6156
MISSISSIPPI STATE, MS 39762-9662 (662)325-7404
divider line
NSF Program(s): EXP PROG TO STIM COMP RES,
ROBUST INTELLIGENCE,
COLLABORATIVE RESEARCH
divider line
Program Reference Code(s): 1045, 9215, HPCC, 7495, 5955, 5979, 1187, 9150
divider line
Program Element Code(s): 9150, 7495, 7298

ABSTRACT

Graphical models, such as Bayesian networks and influence diagrams, provide principled approaches to solving reasoning and decision making under uncertainty problems. However, the adaptability and scalability of existing methods for these graphical models are often limited. This project aims to address some of these limitations by developing new and improved approaches to explanation, decision making, and learning in graphical models. It includes the following specific objectives: (1) developing new approaches to finding explanations that only contain the most relevant variables for given observations in Bayesian networks, (2) developing heuristic search-based methods and algorithms to solve influence diagrams more efficiently, (3) developing new algorithms for learning optimal Bayesian networks guided by domain-specific heuristic information so that only a small fraction of the solution space need to be explored, and (4) applying the methods developed in this project to real-world applications including multiple-fault diagnosis, supply chain risk management, and online collaborative learning.

This project can lead to significantly better approaches to reasoning and decision making under uncertainty in many disciplines where graphical models have found successful applications, including medicine, security, planning, business, economics, education, and many others. This project can also lead to the development of new and enhanced courses and curricula, the involvement of students from underrepresented groups in the research, and a wide dissemination of the research outcomes through free software, publications, and presentations.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.


Changhe Yuan, Heejin Lim, and Michael L. Littman. "Most relevant explanation: Computational complexity and approximation methods," Annals of Mathematics and Artificial Intelligence, v.61, 2011, p. 159. 

Changhe Yuan, Heejin Lim, and Tsai-Ching Lu. "Most Relevant Explanation in Bayesian Networks," Journal of Artificial Intelligence Research, v.42, 2011, p. 309.

BOOKS/ONE TIME PROCEEDING

Brandon Malone, Changhe Yuan,
and Eric Hansen. "Improving the scalability of
optimal bayesian
network learning with frontier
breadth-first branch and bound
search", 08/15/2010-07/31/2011, "Proceedings of the 27th
Conference on Uncertainty in
Artificial Intelligence (UAI-11)"
,  2011, "2011".

Brandon Malone, Changhe Yuan,
and Eric Hansen. "Memory-efficient dynamic
programming for
learning optimal bayesian
networks", 08/15/2010-07/31/2011, "Proceedings of the 25th AAAI
Conference on Artificial Intel-
ligence (AAAI-11)"
,  2011, "2011".

Changhe Yuan, Feng Cheng, Henry
Dao, Markus Ettl, Grace Lin, and
Karthik Sourirajan. "A
Bayesian approach for supply
chain risk management using
business process standards", 08/15/2010-07/31/2011, "Handbook of Integrated Risk
Management in Global Supply
Chains"
,  2011, "2011".

Changhe Yuan, Brandon Malone,
and Xiojian Wu. "Learning optimal bayesian
networks using a*
search", 08/15/2010-07/31/2011, "Proceedings of the 22nd
International Joint Conference
on Artificial Intelligence
(IJCAI-
11)"
,  2011, "2011".

Brandon Malone, Changhe Yuan,
and Eric Hansen. "Improving the scalability of
optimal bayesian
network learning with frontier
breadth-first branch and bound
search", 08/01/2011-07/31/2012, "Proceedings of the 27th
Conference on Uncertainty in
Artificial Intelligence (UAI-11)"
,  2011, "2011".

Brandon Malone, Changhe Yuan,
and Eric Hansen. "Memory-efficient dynamic
programming for
learning optimal bayesian
networks", 08/01/2011-07/31/2012, "Proceedings of the 25th AAAI
Conference on Artificial Intel-
ligence (AAAI-11)"
,  2011, "2011".

Changhe Yuan, Feng Cheng, Henry
Dao, Markus Ettl, Grace Lin, and
Karthik Sourirajan. "A
Bayesian approach for supply
chain risk management using
business process standards", 08/01/2011-07/31/2012, "Handbook of Integrated Risk
Management in Global Supply
Chains"
,  2011, "2011".

Changhe Yuan, Brandon Malone,
and Xiojian Wu. "Learning optimal bayesian
networks using a*
search", 08/01/2011-07/31/2012, "Proceedings of the 22nd
International Joint Conference
on Artificial Intelligence
(IJCAI-
11)"
,  2011, "2011".

Arindam Khaled, Changhe Yuan,
Eric A. Hansen. "Solving Limited-Memory Influence
Diagrams Using Branch-and-Bound
Search", 08/01/2011-07/31/2012, "Proceedings of the International Symposium on AI
and Mathematics 2012"
,  2012, "2012".

 

Please report errors in award information by writing to: awardsearch@nsf.gov.

 

 

Print this page
Back to Top of page
  FUNDING   AWARDS   DISCOVERIES   NEWS   PUBLICATIONS   STATISTICS   ABOUT NSF   FASTLANE  
Research.gov  |  USA.gov  |  National Science Board  |  Recovery Act  |  Budget and Performance  |  Annual Financial Report
Web Policies and Important Links  |  Privacy  |  FOIA  |  NO FEAR Act  |  Inspector General  |  Webmaster Contact  |  Site Map
National Science Foundation Logo
The National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230, USA
Tel: (703) 292-5111, FIRS: (800) 877-8339 | TDD: (800) 281-8749
  Text Only Version