Abstract
The article is devoted to the development of neurocognitive models of the processes of understanding natural language statements that describe the goals, essential conditions and the course of the mission of autonomous robots. Such models serve to provide a dialogue interface in human-robotic teams. The key problem of automatic formation of a plan for achieving goals common for all members of such a team is solved on the basis of using algorithms for synthesizing the optimal path in a single decision graph based on self-organization processes occurring in the multi-agent neurocognitive architecture we have developed. This approach makes it possible to take into account the semantics of natural language messages and the implicit information contained in them when constructing the desired plan.
The work was supported by RFBR grant No. 19-01-00648.
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Nagoev, Z.V., Nagoeva, O.V., Pshenokova, I.A., Bzhikhatlov, K.C., Gurtueva, I.A., Kankulov, S.A. (2022). Multiagent Neurocognitive Models of the Processes of Understanding the Natural Language Description of the Mission of Autonomous robots. In: Klimov, V.V., Kelley, D.J. (eds) Biologically Inspired Cognitive Architectures 2021. BICA 2021. Studies in Computational Intelligence, vol 1032. Springer, Cham. https://doi.org/10.1007/978-3-030-96993-6_36
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