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Mental Actions and Modelling of Reasoning in Semiotic Approach to AGI

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11654)

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.

Keywords

  • Cognitive agent
  • Sign-based world model
  • Semiotic network
  • Modeling of reasoning

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Fig. 1.

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Acknowledgements

The reported study was supported by RFBR, research Projects No. 18-07-01011 and No. 18-29-22027.

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Correspondence to Aleksandr I. Panov .

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Kovalev, A.K., Panov, A.I. (2019). Mental Actions and Modelling of Reasoning in Semiotic Approach to AGI. In: Hammer, P., Agrawal, P., Goertzel, B., Iklé, M. (eds) Artificial General Intelligence. AGI 2019. Lecture Notes in Computer Science(), vol 11654. Springer, Cham. https://doi.org/10.1007/978-3-030-27005-6_12

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

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