DC FieldValueLanguage
dc.contributor.authorDragović, Brankoen
dc.date.accessioned2020-12-11T13:04:44Z-
dc.date.available2020-12-11T13:04:44Z-
dc.date.issued2019-01-01en
dc.identifier.issn03545180en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/4438-
dc.description.abstract© 2019, University of Nis. All rights reserved. Motivated by successful p-adic modeling of the genetic code, in this paper a p-adic (ultrametric) language is introduced. This language can serve as an effective and advanced constructive element of a future artificial intelligence. In this geometric approach to artificial language, we mainly use p-adic distance as the most powerful example of ultrametrics. Here p-adic distance is a tool to quantify similarity between words – similarity between their structure and similarity between their meaning. As a simple illustration, a p-adic language with four letters and their 3-letter words is presented.en
dc.relation.ispartofFilomaten
dc.subjectP-adic artificial language | P-adic distance | P-adic similarityen
dc.titleTowards p-adic artificial languageen
dc.typeArticleen
dc.identifier.doi10.2298/FIL1904227Den
dc.identifier.scopus2-s2.0-85078307294en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85078307294en
dc.relation.firstpage1227en
dc.relation.lastpage1233en
dc.contributor.orcid#NODATA#en
dc.relation.issue4en
dc.relation.volume33en
item.cerifentitytypePublications-
item.openairetypeArticle-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
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