Learning the Semantics of Notational Systems with a Semiotic Cognitive Automaton
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Through semiotic modelling, a system can retrieve and manipulate its own representational formats to interpret a series of observations; this is in contrast to information processing approaches that require representational formats to be specified beforehand and thus limit the semantic properties that the system can experience. Our semiotic cognitive automaton is driven only by the observations it makes and therefore operates based on grounded symbols. A best-case scenario for our automaton involves observations that are univocally interpreted—i.e. distinct observation symbols—and that make reference to a reality characterised by “hard constraints”. Arithmetic offers such a scenario. The gap between syntax and semantics is also subtle in the case of calculations. Our automaton starts without any a priori knowledge of mathematical formalisms and not only learns the syntactical rules by which arithmetic operations are solved but also reveals the true meaning of numbers by means of second-order reasoning.
KeywordsSymbol grounding problem Semiotic modelling Second-order reasoning Extended correlation Desmogram Abductive reasoning
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Conflict of Interest
Valerio Targon declares that he has no conflict of interest
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.
Human and Animal Rights
This article does not contain any studies with human participants or animals performed by the author.
- 2.Hofstadter D. The ineradicable Eliza effect and its dangers. In Hofstadter D, editor. Fluid concepts and creative analogies: computer models of the fundamental mechanisms of thought. New York: Basic Books; 1994. p. 155–168.Google Scholar
- 8.Devlin KJ. Introduction to mathematical thinking. Palo Alto: Keith Devlin; 2012.Google Scholar
- 11.Gomes A, Gudwin R, El-Hani C, Queiroz J. Towards the emergence of meaning processes in computers from Peircean semiotics. Mind Soc Cogn Stud Econ Soc Sci. 2007;6:173–87.Google Scholar
- 12.Meystel A. Multiresolutional semiotic systems. In: Proceedings of the IEEE international symposium on intelligent control/intelligent systems and semiotics; 1999. p. 198–202.Google Scholar
- 15.Bloom B, editor. Taxonomy of educational objectives: book I, cognitive domain. New York: Longman Green; 1956.Google Scholar
- 17.Hoshen Y, Peleg S. Visual learning of arithmetic operations. CoRR, 2015; abs/1506.02264.Google Scholar
- 18.Hofstadter D. How Raymond Smullyan inspired my 1112-year-old self. In: Smullyan R, Rosenhouse J, editors. Four lives: a celebration of Raymond Smullyan. New York: Dover Publications; 2014.Google Scholar
- 21.Wang P. Embodiment: Does a laptop have a body? In: Proceedings of AGI conference, Arlington, Virginia, USA; p. 174–179; March 2009.Google Scholar
- 22.de Saussure F. Grundfragen der allgemeinen Sprachwissenschaft. Berlin: de Gruyter; 1915. Charles Bally unter Mitw. von Albert Riedlinger, editors, translator Herman Lommel (2001).Google Scholar
- 23.Wall L. Perl language reference manual—for Perl version 5.12.1. Network Theory Ltd; 2010.Google Scholar
- 24.Burch R. Charles Sanders Peirce. In: Zalta EN, editor. The Stanford encyclopedia of philosophy (Winter 2014 edition). 2014. http://plato.stanford.edu/archives/win2014/entries/peirce/.
- 25.Nozawa E. Peircean semeiotic—a 21st century scientific methodology. In: Proceedings of the international symposium on collaborative technologies and systems, Orlando, FL, USA; p. 224–235, May 2007.Google Scholar
- 26.Barthes R. Elements of semiology. London: Jonathan Cape, 1964. trans. Lavers A and Smith C (1967).Google Scholar
- 27.Rieger BB. Computing fuzzy semantic granules from natural language texts. A computational semiotics approach to understanding word meanings. In: Hamza M, editor. Proceedings of the IASTED international conference on artificial intelligence and soft computing, Honolulu, Hawaii, USA; p. 475–479, August 1999.Google Scholar
- 28.Berkhin P. Survey of clustering data mining techniques. Technical report. San Jose: Accrue Software, Inc.; 2002.Google Scholar
- 31.Lengnink K, Schlimm D. Learning and understanding numeral systems: semantic aspects of number representations from an educational perspective. In: Löwe B, Müller T, editors. Philosophy of mathematics: sociological aspects and mathematical practice. London: College Publications; 2010. p. 235–264.Google Scholar
- 33.Russell B. The problems of philosophy. Oxford: Oxford University Press; 1959.Google Scholar
- 34.Mitchell TM. The need for biases in learning generalizations. In: Shavlik JW, Dietterich TG, editors. Readings in machine learning. Los Altos: Morgan Kauffman; 1980. p. 184–191.Google Scholar
- 36.Konderak P. On a cognitive model of semiosis. Stud Log Gramm Rhetor. 2015;40:129–44.Google Scholar
- 37.Colton S. Refactorable numbers: a machine invention. J Integer Sequ. 1999;2, Art. ID 99.1.2. http://eudml.org/doc/226761.
- 40.Lamb SM. On the mechanization of syntactic analysis. In: 1961 Conference on machine translation of languages and applied language analysis, national physical laboratory symposium no. 13, London, Her Majesty’s Stationery Office, 1961. vol. 2, p. 674–685.Google Scholar