A Framework for efficient computation of belief theoretic operations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The Dempster-Shafer (DS) theory is a powerful general framework for reasoning under uncertainty. While the strength of the DS theoretic (DST) framework in its ability to handle a wider variety of data imperfections is not in dispute, a major criticism cast towards DST evidential reasoning is the heavy computational burden it entails. If the advantages offered by DS theory is to be fully realized, it is essential that one explores efficient data structures and algorithms that can be used for DST operations and computations. In this paper, we wish to present a novel generalized computational framework for exactly this purpose. We develop three representations - DS-Vector, DS-Matrix, and DS-Tree - which allow DST computation to be performed in significantly less time. These three representations can also be utilized as tools for visualizing DST models. A new strategy, which we refer to as REGAP, which allows REcursive Generation of and Access to Propositions is introduced and harnessed in the development of this framework and computational algorithms. The paper also provides a discussion and experimental validation of the utility, efficiency, and implementation of the proposed data structures and algorithms.

Original languageEnglish (US)
Title of host publicationFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1570-1577
Number of pages8
ISBN (Electronic)9780996452748
StatePublished - Aug 1 2016
Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany
Duration: Jul 5 2016Jul 8 2016

Other

Other19th International Conference on Information Fusion, FUSION 2016
CountryGermany
CityHeidelberg
Period7/5/167/8/16

Fingerprint

Algorithms and Data Structures
Dempster-Shafer Theory
Data structures
Reasoning under Uncertainty
Evidential Reasoning
Experimental Validation
Computational Algorithm
Imperfections
Proposition
Defects
Framework
Beliefs
Dempster-Shafer theory
Model
Uncertainty
Strategy
Burden
Dispute
Evidential reasoning
Criticism

Keywords

  • algorithms
  • belief functions
  • computational frameworks
  • data structures
  • Dempster-Shafer theory
  • evidential reasoning

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Polpitiya, L. G., Premaratne, K., Murthi, M., & Sarkar, D. (2016). A Framework for efficient computation of belief theoretic operations. In FUSION 2016 - 19th International Conference on Information Fusion, Proceedings (pp. 1570-1577). [7528070] Institute of Electrical and Electronics Engineers Inc..

A Framework for efficient computation of belief theoretic operations. / Polpitiya, Lalintha G.; Premaratne, Kamal; Murthi, Manohar; Sarkar, Dilip.

FUSION 2016 - 19th International Conference on Information Fusion, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1570-1577 7528070.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Polpitiya, LG, Premaratne, K, Murthi, M & Sarkar, D 2016, A Framework for efficient computation of belief theoretic operations. in FUSION 2016 - 19th International Conference on Information Fusion, Proceedings., 7528070, Institute of Electrical and Electronics Engineers Inc., pp. 1570-1577, 19th International Conference on Information Fusion, FUSION 2016, Heidelberg, Germany, 7/5/16.
Polpitiya LG, Premaratne K, Murthi M, Sarkar D. A Framework for efficient computation of belief theoretic operations. In FUSION 2016 - 19th International Conference on Information Fusion, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1570-1577. 7528070
Polpitiya, Lalintha G. ; Premaratne, Kamal ; Murthi, Manohar ; Sarkar, Dilip. / A Framework for efficient computation of belief theoretic operations. FUSION 2016 - 19th International Conference on Information Fusion, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1570-1577
@inproceedings{61f58f83cb534600b32a60899e83a88b,
title = "A Framework for efficient computation of belief theoretic operations",
abstract = "The Dempster-Shafer (DS) theory is a powerful general framework for reasoning under uncertainty. While the strength of the DS theoretic (DST) framework in its ability to handle a wider variety of data imperfections is not in dispute, a major criticism cast towards DST evidential reasoning is the heavy computational burden it entails. If the advantages offered by DS theory is to be fully realized, it is essential that one explores efficient data structures and algorithms that can be used for DST operations and computations. In this paper, we wish to present a novel generalized computational framework for exactly this purpose. We develop three representations - DS-Vector, DS-Matrix, and DS-Tree - which allow DST computation to be performed in significantly less time. These three representations can also be utilized as tools for visualizing DST models. A new strategy, which we refer to as REGAP, which allows REcursive Generation of and Access to Propositions is introduced and harnessed in the development of this framework and computational algorithms. The paper also provides a discussion and experimental validation of the utility, efficiency, and implementation of the proposed data structures and algorithms.",
keywords = "algorithms, belief functions, computational frameworks, data structures, Dempster-Shafer theory, evidential reasoning",
author = "Polpitiya, {Lalintha G.} and Kamal Premaratne and Manohar Murthi and Dilip Sarkar",
year = "2016",
month = "8",
day = "1",
language = "English (US)",
pages = "1570--1577",
booktitle = "FUSION 2016 - 19th International Conference on Information Fusion, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - A Framework for efficient computation of belief theoretic operations

AU - Polpitiya, Lalintha G.

AU - Premaratne, Kamal

AU - Murthi, Manohar

AU - Sarkar, Dilip

PY - 2016/8/1

Y1 - 2016/8/1

N2 - The Dempster-Shafer (DS) theory is a powerful general framework for reasoning under uncertainty. While the strength of the DS theoretic (DST) framework in its ability to handle a wider variety of data imperfections is not in dispute, a major criticism cast towards DST evidential reasoning is the heavy computational burden it entails. If the advantages offered by DS theory is to be fully realized, it is essential that one explores efficient data structures and algorithms that can be used for DST operations and computations. In this paper, we wish to present a novel generalized computational framework for exactly this purpose. We develop three representations - DS-Vector, DS-Matrix, and DS-Tree - which allow DST computation to be performed in significantly less time. These three representations can also be utilized as tools for visualizing DST models. A new strategy, which we refer to as REGAP, which allows REcursive Generation of and Access to Propositions is introduced and harnessed in the development of this framework and computational algorithms. The paper also provides a discussion and experimental validation of the utility, efficiency, and implementation of the proposed data structures and algorithms.

AB - The Dempster-Shafer (DS) theory is a powerful general framework for reasoning under uncertainty. While the strength of the DS theoretic (DST) framework in its ability to handle a wider variety of data imperfections is not in dispute, a major criticism cast towards DST evidential reasoning is the heavy computational burden it entails. If the advantages offered by DS theory is to be fully realized, it is essential that one explores efficient data structures and algorithms that can be used for DST operations and computations. In this paper, we wish to present a novel generalized computational framework for exactly this purpose. We develop three representations - DS-Vector, DS-Matrix, and DS-Tree - which allow DST computation to be performed in significantly less time. These three representations can also be utilized as tools for visualizing DST models. A new strategy, which we refer to as REGAP, which allows REcursive Generation of and Access to Propositions is introduced and harnessed in the development of this framework and computational algorithms. The paper also provides a discussion and experimental validation of the utility, efficiency, and implementation of the proposed data structures and algorithms.

KW - algorithms

KW - belief functions

KW - computational frameworks

KW - data structures

KW - Dempster-Shafer theory

KW - evidential reasoning

UR - http://www.scopus.com/inward/record.url?scp=84992129668&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992129668&partnerID=8YFLogxK

M3 - Conference contribution

SP - 1570

EP - 1577

BT - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -