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Summary
This summary is machine-generated.

Achieving dynamic interoperability in complex systems of systems (SoS) remains challenging. Recent machine learning advancements show promise for enabling purposeful cyber-physical system (CPS) communication, but unifying solutions are still needed.

Keywords:
AI for cyber-physical systemsdynamic interoperabilityrepresentation learningsystem of systems

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Area of Science:

  • Cyber-Physical Systems Engineering
  • Artificial Intelligence
  • Computer Science

Background:

  • Interoperability is crucial for System of Systems (SoS) and cyber-physical systems (CPS), enabling information exchange and cooperation.
  • Dynamically establishing interoperability between heterogeneous CPS at run-time is a complex challenge.
  • Existing solutions are often domain-specific, lacking unifying approaches to address variable system relations.

Purpose of the Study:

  • To survey literature for machine learning (ML) and connecting approaches for automatically establishing SoS with purposeful CPS communication.
  • To identify and integrate concepts not currently combined in existing research.
  • To explore AI-enabled solutions for dynamic SoS interoperability.

Main Methods:

  • Literature survey focusing on ML and related fields.
  • Summarizing recent developments in representation learning, neural networks, concept learning, and emergent communication.
  • Analyzing AI-enabled solutions and their relation to SoS interoperability requirements.

Main Results:

  • A growing interest in deep learning for establishing communication under diverse environmental and entity assumptions.
  • Identification of relevant ML techniques such as representation learning for ontology alignment and neural networks for transcoding.
  • Examples of architectures and discussion of open problems in AI-enabled SoS interoperability.

Conclusions:

  • Despite recent ML advancements, a unified approach to dynamic interoperability in complex SoS is still lacking.
  • Further research is needed to bridge existing concepts and develop realistic testbeds for AI-driven solutions.
  • The integration of ML offers new research avenues for purposeful CPS communication and SoS interoperability.