Abstract
The purpose of the research is the development of the theoretical foundations and algorithms for the unstructured data flows recognition based on multi-agent neurocognitive architectures in artificial intelligence systems. The task of the study is to develop a simulation model of the cognitive function of static objects recognition according to the video stream.
The article developed a simulation model of the cognitive function of static objects recognition based on multi-agent architectures and a software system that demonstrates its work in geometric shapes recognition. The simulation model based on multi-agent neurocognitive architectures allows to create concepts and categories in the autonomous mode according to the data of multimodal input information (event occurred). Through interaction with the user, the system can expand these concepts and categories, and correct the links between them.
The system presented in the paper is autonomous and self-learning. It can be used in autonomous artificial intelligence systems, such as Smart Systems, robotic complexes, etc., to recognize unstructured data streams.
The work was supported by RFBR grants №18-01-00658, 19-01-00648.
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Nagoev, Z., Pshenokova, I., Gurtueva, I., Bzhikhatlov, K. (2020). A Simulation Model for the Cognitive Function of Static Objects Recognition Based on Machine-Learning Multi-agent Architectures. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_48
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DOI: https://doi.org/10.1007/978-3-030-25719-4_48
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