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Scientific paper - Chapter in Book
Why Not Fuzzy Logic?
In: Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives / Skansi, Sandro (Ed.). Cham: Springer Nature Switzerland AG, 2020. pp. 29-40. urn:nbn:hr:261:520402
Restović, Ivan

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Restović, I. (2020). Why Not Fuzzy Logic?. In S. Skansi, (Ed.), Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives (pp. 29-40). Cham: Springer Nature Switzerland AG. Retrieved from https://urn.nsk.hr/urn:nbn:hr:261:520402

Restović, Ivan. "Why Not Fuzzy Logic?." Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives, edited by Sandro Skansi, Springer Nature Switzerland AG, 2020, pp. 29-40. https://urn.nsk.hr/urn:nbn:hr:261:520402

Restović, Ivan. "Why Not Fuzzy Logic?." In Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives, edited by Sandro Skansi, 29-40. Cham: Springer Nature Switzerland AG, 2020. https://urn.nsk.hr/urn:nbn:hr:261:520402

Restović, I. (2020) 'Why Not Fuzzy Logic?' in Skansi, S. (ed.) Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives Cham: Springer Nature Switzerland AG, pp. 29-40. Available at: https://urn.nsk.hr/urn:nbn:hr:261:520402 (Accessed 13 December 2024)

Restović I. Why Not Fuzzy Logic?. In: S. Skansi, ed. Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives. Cham: Springer Nature Switzerland AG; 2020. Pp. 29-40. [cited 2024 December 13] Available at: https://urn.nsk.hr/urn:nbn:hr:261:520402

I. Restović, "Why Not Fuzzy Logic?", Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives, S. Skansi, Ed. Cham: Springer Nature Switzerland AG, 2020, pp. 29-40. [Online] Available at: https://urn.nsk.hr/urn:nbn:hr:261:520402

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Metadata
Title (english)Why Not Fuzzy Logic?
AuthorIvan Restović
EditorSandro Skansi
Author's institutionInstitute of Philosophy
Scientific / art field,
discipline and subdiscipline		HUMANISTIC SCIENCES
Philosophy
Logic
Abstract (english)
Fuzzy logic is an approach to AI which focuses on the mechanization of natural language. It has long been proposed by Zadeh, its originator, as another paradigm for AI and the correct way to achieve "human level machine intelligence". To present day, this approach hasn't prevailed, but in the light of some recent tendencies in AI development it can gain traction. The "black-box property" of the currently predominant method — deep learning — has recently sparked a movement called "explainable artificial intelligence", a quest for AI that can explain its decisions in a way understandable and acceptable to humans. As it has been recognized, a natural way to provide explanations to users is to use natural language, embedded in the fuzzy logic paradigm. However, to model natural language fuzzy logic uses the notion of "partial truth", which has brought some philosophical concerns. The very core tenets of fuzzy logic have often been described as counterintuitive. In this text, we provide philosophical support for fuzzy logic by providing possible answers to the two most common critiques raised about it, as well as by offering independent philosophical motivation for endorsing it.
Keywords (english)
Languageenglish
Publication typeScientific paper - Chapter in Book
Publication statusPublished
Peer reviewPeer review
Publication versionPublished version
Book titleGuide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives
Numberingpp. 29-40
ISBN978-3-030-37590-4
URN:NBNurn:nbn:hr:261:520402
Publication2020
Type of resourceText
PublisherSpringer Nature Switzerland AG
Publishing placeCham
Access conditionsClosed access
Terms of useLicence In copyright icon
Created on2022-10-17 14:19:27