Browsing by Author "Kao, HY"

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A diagnostic reasoning and optimal treatment model for bacteria infections with fuzzy information
(2005-01-01)
Kao, HY
;
Li, HL
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This study proposes an optimization model for optimal treatment of bacterial infections. Using an influence diagram as the knowledge and decision model, we can conduct two kinds of reasoning simultaneously: diagnostic reasoning and treatment planning. The input information of the reasoning system are conditional probability distributions of the network model, the costs of the candidate antibiotic treatments, the expected effects of the treatments, and extra constraints regarding belief propagation. Since the prevalence of the pathogens and infections are determined by many site-by-site factors, which are not compliant with conventional approaches for approximate reasoning, we introduce fuzzy information. The output results of the reasoning model are the Likelihood of a bacterial infection, the most likely pathogen(s), the suggestion of optimal treatment, the gain of life expectancy for the patient related to the optimal treatment, the probability of coverage associated with the antibiotic treatment, and the cost-effect analysis of the treatment prescribed. (C) 2004 Elsevier Ireland Ltd. All rights reserved.
Answering queries on graphical decision models with fuzzy parameters: Algorithms and comparisons
(2004)
Kao, HY
;
Huang, CH
;
Li, HL
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
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 alpha-cut methods will be implemented and compared.
Constrained abductive reasoning with fuzzy parameters in Bayesian networks
(2005-01-01)
Li, HL
;
Kao, HY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This work proposes a novel approach for solving abductive reasoning problems in Bayesian networks involving fuzzy parameters and extra constraints. The proposed method formulates abduction problems using nonlinear programming. To maximize the sum of the fuzzy membership functions subjected to various constraints, such as boundary, dependency and disjunctive conditions, unknown node belief propagation is completed. The model developed here can be built on any exact propagation methods, including clustering, joint tree decomposition, etc. (C) 2003 Elsevier Ltd. All rights reserved.
Supply chain diagnostics with dynamic Bayesian networks
(2005-09-01)
Kao, HY
;
Huang, CH
;
Li, HL
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This paper proposes a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology developed by Naim, Childerhouse, Disney, and Towill (2002). [A supply chain diagnostic methodlogy: Determing the vector of change. Computers and Industrial Engineering, 43, 135-157], a dynamic Bayesian network is employed as a more descriptive mechanism to model the causal relationships in the supply chain. Dynamic Bayesian networks can be utilized as a knowledge base of the reasoning systems where the diagnostic tasks are conducted. We finally solve this reasoning problem with stochastic simulation. (c) 2005 Elsevier Ltd. All rights reserved.