NDLI: A fast hill-climbing approach without an energy function for probabilistic reasoning

Content Provider	IEEE Xplore Digital Library
Author	Santos Jr., E.
Copyright Year	1993
Description	Author affiliation: Dept. of Electr. & Comput. Eng., US Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA (Santos, E., Jr.)
Abstract	Integer linear programming (ILP) has long been an important tool for operations research akin to the AI search heuristics for NP-hard problems. However, there has been relatively little incentive to use it in AI, even though it also deals with optimization. The problem stems from the misperception that because the general ILP problem is difficult to solve, then it will be difficult for all cases. It is known that AI search at first glance also seems this way until one begins to apply it to a specific domain. Clearly, there are many gains to be had from studying the problem with a different perspective like ILP. The authors look at probabilistic reasoning with Bayesian networks. For some time now, they have been stalled by its computational complexities. Algorithms have been designed for small classes of networks, but have been mainly inextensible to the general case. In particular, the authors consider belief revision in Bayesian networks which is the search for the most probable explanation for some given evidence. They present a new approach for computing belief revision from the ILP point of view. By observing various properties inherent in Bayesian networks, one can successfully develop a hill-climbing strategy which does not require an energy function. This approach can handle the entire class of Bayesian networks. Furthermore, experimental results indicate that finding the most probable explanation can be accomplished fairly easily.
Starting Page	170
Ending Page	179
File Size	924951
Page Count	10
File Format	PDF
ISBN	0818642009
ISSN	10636730
DOI	10.1109/TAI.1993.633953
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-11-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial intelligence Bayesian methods Power system modeling Integer linear programming Operations research Operating systems Computational complexity Heart Intelligent systems Computational intelligence
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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