Answering queries on graphical decision models with fuzzy parameters: Algorithms and comparisons

Han Ying Kao; Chia Hui Huang; Han-Lin Li

Answering queries on graphical decision models with fuzzy parameters: Algorithms and comparisons

Han Ying Kao^*, Chia Hui Huang, Han-Lin Li

^*Corresponding author for this work

Institute of Information Management

Research output: Contribution to journal › Conference article › peer-review

Abstract

The reasoning strategy is a critical component for expert systems or decision support systems. After thorough self-evaluation and environmental scan, the knowledge workers need a reliable mechanism for diagnosis and an optimal suggestion for problem solving. This paper designs a reasoning model with a well-known graphical decision model, influence diagrams. In the proposed model, fuzzy probabilities as well as crisp probabilities are involved. The influence diagrams work compactly in problem diagnosis and decision making aid. Two algorithms to handle the fuzzy parameters, piecewise linearization and α-cut methods will be implemented and compared.

Original language	English
Pages (from-to)	148-153
Number of pages	6
Journal	Proceedings of the IASTED International Conference on Modelling, Simulation, and Optimization
State	Published - 2004
Event	Proceedings of the Fourth IASTED International Conference on Modelling, Simulation, and Optimization - Kauai, HI, United States Duration: 17 Aug 2004 → 19 Aug 2004

Keywords

Decision making
Fuzzy systems
Influence diagrams
Multi-objective optimization
Reasoning strategy

Cite this

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title = "Answering queries on graphical decision models with fuzzy parameters: Algorithms and comparisons",

abstract = "The reasoning strategy is a critical component for expert systems or decision support systems. After thorough self-evaluation and environmental scan, the knowledge workers need a reliable mechanism for diagnosis and an optimal suggestion for problem solving. This paper designs a reasoning model with a well-known graphical decision model, influence diagrams. In the proposed model, fuzzy probabilities as well as crisp probabilities are involved. The influence diagrams work compactly in problem diagnosis and decision making aid. Two algorithms to handle the fuzzy parameters, piecewise linearization and α-cut methods will be implemented and compared.",

keywords = "Decision making, Fuzzy systems, Influence diagrams, Multi-objective optimization, Reasoning strategy",

author = "Kao, {Han Ying} and Huang, {Chia Hui} and Han-Lin Li",

year = "2004",

language = "English",

pages = "148--153",

journal = "Proceedings of the IASTED International Conference on Modelling, Simulation, and Optimization",

issn = "1021-8181",

publisher = "ACTA Press",

note = "Proceedings of the Fourth IASTED International Conference on Modelling, Simulation, and Optimization ; Conference date: 17-08-2004 Through 19-08-2004",

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T1 - Answering queries on graphical decision models with fuzzy parameters

T2 - Proceedings of the Fourth IASTED International Conference on Modelling, Simulation, and Optimization

AU - Kao, Han Ying

AU - Huang, Chia Hui

AU - Li, Han-Lin

PY - 2004

Y1 - 2004

N2 - The reasoning strategy is a critical component for expert systems or decision support systems. After thorough self-evaluation and environmental scan, the knowledge workers need a reliable mechanism for diagnosis and an optimal suggestion for problem solving. This paper designs a reasoning model with a well-known graphical decision model, influence diagrams. In the proposed model, fuzzy probabilities as well as crisp probabilities are involved. The influence diagrams work compactly in problem diagnosis and decision making aid. Two algorithms to handle the fuzzy parameters, piecewise linearization and α-cut methods will be implemented and compared.

AB - The reasoning strategy is a critical component for expert systems or decision support systems. After thorough self-evaluation and environmental scan, the knowledge workers need a reliable mechanism for diagnosis and an optimal suggestion for problem solving. This paper designs a reasoning model with a well-known graphical decision model, influence diagrams. In the proposed model, fuzzy probabilities as well as crisp probabilities are involved. The influence diagrams work compactly in problem diagnosis and decision making aid. Two algorithms to handle the fuzzy parameters, piecewise linearization and α-cut methods will be implemented and compared.

KW - Decision making

KW - Fuzzy systems

KW - Influence diagrams

KW - Multi-objective optimization

KW - Reasoning strategy

UR - http://www.scopus.com/inward/record.url?scp=10444223847&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:10444223847

SN - 1021-8181

SP - 148

EP - 153

JO - Proceedings of the IASTED International Conference on Modelling, Simulation, and Optimization

JF - Proceedings of the IASTED International Conference on Modelling, Simulation, and Optimization

Y2 - 17 August 2004 through 19 August 2004

ER -

Answering queries on graphical decision models with fuzzy parameters: Algorithms and comparisons

Abstract

Keywords

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