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Cognitive Computation
Published in: Cognitive Computation 4/2016

01-08-2016

Learning the Semantics of Notational Systems with a Semiotic Cognitive Automaton

Author: Valerio Targon

Published in: Cognitive Computation | Issue 4/2016

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Abstract

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.
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Footnotes
1
I learned of this use of the extended correlation from the Italian computer scientist Piero Slocovich.
 
2
This exception is represented by the regular expressions that contain the impossible sequences “;0”, “=0” and so on, which are already known from the first iteration (see Table 2).
 
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Metadata
Title
Learning the Semantics of Notational Systems with a Semiotic Cognitive Automaton
Author
Valerio Targon
Publication date
01-08-2016
Publisher
Springer US
Published in
Cognitive Computation / Issue 4/2016
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-015-9378-0

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