Expert system models for inference with imperfect knowledge: A comparative study
type: Item
instance of: scholarly article
title: Expert system models for inference with imperfect knowledge: A comparative study
zbMATH Open document ID: 0656.68113
DOI: 10.1016/0378-3758(88)90092-4
author: Ambrose Goicoechea
published in: Journal of Statistical Planning and Inference
publication date: 1988-01-01T00:00:00Z
review text: This paper presents a detailed comparative study of six major, leading methods for reasoning based on imperfect knowledge: (1) Bayes' rule, (2) Dempster-Shafer theory, (3) fuzzy set theory, (4) MYCIN Model, (5) Cohen's system of inductive probabilities, and (6) a class of non- monotonic reasoning methods. Each method is presented and discussed in terms of theoretical content, a detailed numerical example, and a list of strengths and limitations. Purposely, the same numerical example is addressed by each method such that we are able to highlight the assumptions, and computational requirements that are specific to each method in a consistent manner.
Mathematics Subject Classification ID: 68T99
Mathematics Subject Classification ID: 62C99
zbMATH DE Number: 4072446
zbMATH Keywords: inference models
zbMATH Keywords: expert systems
zbMATH Keywords: imperfect knowledge
zbMATH Keywords: uncertainty
zbMATH Keywords: decision support systems
zbMATH Keywords: inference network
zbMATH Keywords: evidential reasoning
zbMATH Keywords: non- monotonic reasoning
MaRDI profile type: Publication
full work available at URL: 0378-3758(88)90092-4
OpenAlex ID: W2008088823
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description: scientific article; zbMATH DE number 4072446