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Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems

  • James A. Crowder
  • John Carbone
  • Shelli Friess
Chapter

Abstract

Self-diagnostics and prognostics in multi-agent processing systems is explored in the context of self-soothing concepts in neuropsychology. This is one of the first steps to facilitate systems-level thinking in AI. Autonomous or semi-autonomous system must be able to understand, at a system-wide level, how every part of the system is influencing the other parts of the system. This drives the need for complete self-assessment within the AI system. The use of emotional memory and autonomic nervous state recall can be used to provide contextual cognition for system-level diagnostic and prognostics in large-scale systems. The use of an artificial cognitive neural framework with intelligent information software agents can be utilized to emulate emotional learning to facilitate self-soothing, which equates to self-healing in artificial neural systems. This chapter describes the architecture and specifications of software agents that are used to provide self-soothing and self-healing constructs for intelligent systems (Flexible object architectures for hybrid neural processing systems, Las Vegas, NV, 2010).

Keywords

Diagnostics Prognostics Artificial intelligence Self-soothing Emotional learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • James A. Crowder
    • 1
  • John Carbone
    • 2
  • Shelli Friess
    • 3
  1. 1.Colorado Engineering Inc.Colorado SpringsUSA
  2. 2.ForcepointAustinUSA
  3. 3.Walden UniversityMinneapolisUSA

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