An extended framework for evidential reasoning systems

Weiru Liu; Jun Hong; Michael McTear

doi:10.1109/TAI.1990.130429

An extended framework for evidential reasoning systems

Weiru Liu, Jun Hong, Michael McTear

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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

Original language	English
Title of host publication	The 2nd International IEEE Conference on Tools for Artificial Intelligence
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	731 - 737
Number of pages	7
ISBN (Print)	0-8186-2084-6
DOIs	https://doi.org/10.1109/TAI.1990.130429
Publication status	Published - 1990

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10.1109/TAI.1990.130429

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title = "An extended framework for evidential reasoning systems",

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",

author = "Weiru Liu and Jun Hong and Michael McTear",

year = "1990",

doi = "10.1109/TAI.1990.130429",

language = "English",

isbn = "0-8186-2084-6",

pages = "731 -- 737",

booktitle = "The 2nd International IEEE Conference on Tools for Artificial Intelligence",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

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T1 - An extended framework for evidential reasoning systems

AU - Liu, Weiru

AU - Hong, Jun

AU - McTear, Michael

PY - 1990

Y1 - 1990

N2 - 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

AB - 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

U2 - 10.1109/TAI.1990.130429

DO - 10.1109/TAI.1990.130429

M3 - Conference contribution

SN - 0-8186-2084-6

SP - 731

EP - 737

BT - The 2nd International IEEE Conference on Tools for Artificial Intelligence

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

An extended framework for evidential reasoning systems

Abstract

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