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Mental Actions and Modelling of Reasoning in Semiotic Approach to AGI

  • 2019
  • OriginalPaper
  • Chapter
Published in:
Authors
Alexey K. Kovalev
Aleksandr I. Panov
Published in
Artificial General Intelligence
Publisher
Springer International Publishing

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Abstract

The article expounds the functional of a cognitive architecture Sign-Based World Model (SBWM) through the algorithm for the implementation of a particular case of reasoning. The SBWM architecture is a multigraph, called a semiotic network with special rules of activation spreading. In a semiotic network, there are four subgraphs that have specific properties and are composed of constituents of the main SBWM element – the sign. Such subgraphs are called causal networks on images, significances, personal meanings, and names. The semiotic network can be viewed as the memory of an intelligent agent. It is proposed to divide the agent’s memory in the SBWM architecture into a long-term memory consisting of signs-prototype, and a working memory consisting of signs-instance. The concept of elementary mental actions is introduced as an integral part of the reasoning process. Examples of such actions are provided. The performance of the proposed reasoning algorithm is considered by a model example.

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previous chapter Programmatic Link Grammar Induction for Unsupervised Language Learning
next chapter Embodiment as a Necessary a Priori of General Intelligence
Title
Mental Actions and Modelling of Reasoning in Semiotic Approach to AGI
Authors
Alexey K. Kovalev
Aleksandr I. Panov
Copyright Year
2019
Book
Artificial General Intelligence

Print ISBN: 978-3-030-27004-9

Electronic ISBN: 978-3-030-27005-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-27005-6_12
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