Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems
| Autor: | James A. Crowder, Shelli Friess, John N. Carbone |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science Neuropsychology Intelligent decision support system Context (language use) Cognition 02 engineering and technology Object (computer science) 020901 industrial engineering & automation Software agent Human–computer interaction 0202 electrical engineering electronic engineering information engineering Prognostics 020201 artificial intelligence & image processing State (computer science) |
| Zdroj: | Artificial Psychology ISBN: 9783030170790 IC-AI |
| DOI: | 10.1007/978-3-030-17081-3_8 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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