An extended framework for evidential reasoning systems

Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's parti...

Full description

Saved in:
Bibliographic Details
Main Authors: Liu, W., Hong, J., McTear, M.F.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Bayesian methods
Expert systems
Fuzzy sets
Graphics
Information systems
Knowledge based systems
Uncertainty
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 737
container_issue
container_start_page 731
container_title
container_volume
creator Liu, W.
Hong, J.
McTear, M.F.
description Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems.< >
doi_str_mv 10.1109/TAI.1990.130429
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_130429</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>130429</ieee_id><sourcerecordid>130429</sourcerecordid><originalsourceid>FETCH-LOGICAL-i172t-32a3df619d6d15a56ae94c21a00562ff45f7974ee1521c53b8ae89593e3784893</originalsourceid><addsrcrecordid>eNotj0tLAzEUhQMiVGvXgqv8gam5eU3ucig-CoVu6rqkzY1EOxlJBrX_3oF6Nodvc_gOY_cglgACH3fdegmIEymhJV6xW-HAWSmctjO2qPVDTDHGIZgbZrvM6XekHCjwWHxPP0P55HEonL5ToDwmf-KFfB1yyu-8nutIfb1j19GfKi3-e87enp92q9dms31Zr7pNk6CVY6OkVyFawGADGG-sJ9RHCX4SsDJGbWKLrSYCI-Fo1MF5cmhQkWqddqjm7OGym4ho_1VS78t5f3mm_gAMzEL9</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An extended framework for evidential reasoning systems</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Liu, W. ; Hong, J. ; McTear, M.F.</creator><creatorcontrib>Liu, W. ; Hong, J. ; McTear, M.F.</creatorcontrib><description>Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems.&lt; &gt;</description><identifier>ISBN: 0818620846</identifier><identifier>ISBN: 9780818620843</identifier><identifier>DOI: 10.1109/TAI.1990.130429</identifier><language>eng</language><publisher>IEEE Comput. Soc. Press</publisher><subject>Artificial intelligence ; Bayesian methods ; Expert systems ; Fuzzy sets ; Graphics ; Information systems ; Knowledge based systems ; Uncertainty</subject><ispartof>[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 1990, p.731-737</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/130429$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/130429$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liu, W.</creatorcontrib><creatorcontrib>Hong, J.</creatorcontrib><creatorcontrib>McTear, M.F.</creatorcontrib><title>An extended framework for evidential reasoning systems</title><title>[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence</title><addtitle>TAI</addtitle><description>Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems.&lt; &gt;</description><subject>Artificial intelligence</subject><subject>Bayesian methods</subject><subject>Expert systems</subject><subject>Fuzzy sets</subject><subject>Graphics</subject><subject>Information systems</subject><subject>Knowledge based systems</subject><subject>Uncertainty</subject><isbn>0818620846</isbn><isbn>9780818620843</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1990</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj0tLAzEUhQMiVGvXgqv8gam5eU3ucig-CoVu6rqkzY1EOxlJBrX_3oF6Nodvc_gOY_cglgACH3fdegmIEymhJV6xW-HAWSmctjO2qPVDTDHGIZgbZrvM6XekHCjwWHxPP0P55HEonL5ToDwmf-KFfB1yyu-8nutIfb1j19GfKi3-e87enp92q9dms31Zr7pNk6CVY6OkVyFawGADGG-sJ9RHCX4SsDJGbWKLrSYCI-Fo1MF5cmhQkWqddqjm7OGym4ho_1VS78t5f3mm_gAMzEL9</recordid><startdate>1990</startdate><enddate>1990</enddate><creator>Liu, W.</creator><creator>Hong, J.</creator><creator>McTear, M.F.</creator><general>IEEE Comput. Soc. Press</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1990</creationdate><title>An extended framework for evidential reasoning systems</title><author>Liu, W. ; Hong, J. ; McTear, M.F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-32a3df619d6d15a56ae94c21a00562ff45f7974ee1521c53b8ae89593e3784893</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1990</creationdate><topic>Artificial intelligence</topic><topic>Bayesian methods</topic><topic>Expert systems</topic><topic>Fuzzy sets</topic><topic>Graphics</topic><topic>Information systems</topic><topic>Knowledge based systems</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Liu, W.</creatorcontrib><creatorcontrib>Hong, J.</creatorcontrib><creatorcontrib>McTear, M.F.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, W.</au><au>Hong, J.</au><au>McTear, M.F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An extended framework for evidential reasoning systems</atitle><btitle>[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence</btitle><stitle>TAI</stitle><date>1990</date><risdate>1990</risdate><spage>731</spage><epage>737</epage><pages>731-737</pages><isbn>0818620846</isbn><isbn>9780818620843</isbn><abstract>Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems.&lt; &gt;</abstract><pub>IEEE Comput. Soc. Press</pub><doi>10.1109/TAI.1990.130429</doi><tpages>7</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 0818620846
ispartof [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 1990, p.731-737
issn
language eng
recordid cdi_ieee_primary_130429
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Artificial intelligence
Bayesian methods
Expert systems
Fuzzy sets
Graphics
Information systems
Knowledge based systems
Uncertainty
title An extended framework for evidential reasoning systems
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T02%3A24%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=An%20extended%20framework%20for%20evidential%20reasoning%20systems&rft.btitle=%5B1990%5D%20Proceedings%20of%20the%202nd%20International%20IEEE%20Conference%20on%20Tools%20for%20Artificial%20Intelligence&rft.au=Liu,%20W.&rft.date=1990&rft.spage=731&rft.epage=737&rft.pages=731-737&rft.isbn=0818620846&rft.isbn_list=9780818620843&rft_id=info:doi/10.1109/TAI.1990.130429&rft_dat=%3Cieee_6IE%3E130429%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i172t-32a3df619d6d15a56ae94c21a00562ff45f7974ee1521c53b8ae89593e3784893%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=130429&rfr_iscdi=true