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Hyperdimensional Representations in Semiotic Approach to AGI

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12177)


The paper is dedicated to the use of distributed hyperdimensional vectors to represent sensory information in the sign-based cognitive architecture, in which the image component of a sign is encoded by a causal matrix. The hyperdimensional representation allows us to update the precedent dimension of the causal matrix and accumulate information in it during the interaction of the system with the environment. Due to the high dimensionality of vectors, it is possible to reduce the representation and reasoning on the entities related to them to simple operations on vectors. In this work we show how hyperdimensional representations are embedded in an existing sign formalism and provide examples of visual scene encoding.


  • Cognitive agent
  • Sign-based world model
  • Semiotic network
  • Causal tensor
  • Distributed representation
  • Symbol grounding

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  • DOI: 10.1007/978-3-030-52152-3_24
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The reported study was supported by RFBR, research Projects No. 18-07-01011 and No. 19-37-90164.

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Correspondence to Aleksandr I. Panov .

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Kovalev, A.K., Panov, A.I., Osipov, E. (2020). Hyperdimensional Representations in Semiotic Approach to AGI. In: Goertzel, B., Panov, A., Potapov, A., Yampolskiy, R. (eds) Artificial General Intelligence. AGI 2020. Lecture Notes in Computer Science(), vol 12177. Springer, Cham.

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