Explaining examples

Fawaz Alsolami; Mohammad Azad; Igor Chikalov; Mikhail Moshkov

doi:10.1007/978-3-030-12854-8_2

Explaining examples

Fawaz Alsolami^*, Mohammad Azad, Igor Chikalov, Mikhail Moshkov

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

In this chapter, we discuss briefly main notions: problems with many-valued decisions, decision tables corresponding to these problems, decision and inhibitory trees, rules, and systems of rules. We consider only depth of trees, and length of rules and systems of rules. After that, we concentrate on consideration of simple examples of problems with many-valued decisions from different areas of applications: fault diagnosis, computational geometry, combinatorial optimization, and analysis of data. At the end, we discuss two examples which explain why we study not only decision but also inhibitory rules and trees.

Original language	English (US)
Title of host publication	Intelligent Systems Reference Library
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	17-36
Number of pages	20
DOIs	https://doi.org/10.1007/978-3-030-12854-8_2
State	Published - 2020

Publication series

Name	Intelligent Systems Reference Library
Volume	156
ISSN (Print)	1868-4394
ISSN (Electronic)	1868-4408

ASJC Scopus subject areas

General Computer Science
Information Systems and Management
Library and Information Sciences

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10.1007/978-3-030-12854-8_2

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AU - Chikalov, Igor

AU - Moshkov, Mikhail

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AB - In this chapter, we discuss briefly main notions: problems with many-valued decisions, decision tables corresponding to these problems, decision and inhibitory trees, rules, and systems of rules. We consider only depth of trees, and length of rules and systems of rules. After that, we concentrate on consideration of simple examples of problems with many-valued decisions from different areas of applications: fault diagnosis, computational geometry, combinatorial optimization, and analysis of data. At the end, we discuss two examples which explain why we study not only decision but also inhibitory rules and trees.

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ER -

Explaining examples

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