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Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems

In: Probability, Combinatorics and Control

Author

Listed:
  • Andrey Kostogryzov
  • Victor Korolev

Abstract

As a result of the analysis of dispatcher intelligence centers and aerial, land, underground, underwater, universal, and functionally focused artificial intelligence robotics systems, the problems of rational control, due to be performed under specific conditions of uncertainties, are chosen for probabilistic study. The choice covers the problems of planning the possibilities of functions performance on the base of monitored information about events and conditions and the problem of robot route optimization under limitations on risk of "failure" in conditions of uncertainties. These problems are resolved with a use of the proposed probabilistic approach. The proposed methods are based on selected probabilistic models (for "black box" and complex systems), which are implemented effectively in wide application areas. The cognitive solving of problems consists in improvements, accumulation, analysis, and use of appearing knowledge. The described analytical solutions are demonstrated by practical examples.

Suggested Citation

  • Andrey Kostogryzov & Victor Korolev, 2020. "Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems," Chapters, in: Andrey Kostogryzov & Victor Korolev (ed.), Probability, Combinatorics and Control, IntechOpen.
    • Handle: RePEc:ito:pchaps:199392
    DOI: 10.5772/intechopen.89168
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    More about this item

    Keywords

    artificial intelligence system; method; probability; risk; uncertainty;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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