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Basic characteristics of a macroscopic measure for detecting abnormal changes in a multiagent system.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • Runtime multiagent systems face dynamic changes impacting performance.
  • Effective runtime monitoring is crucial for system management and control.
  • Existing observation methods may not adequately detect system anomalies.

Purpose of the Study:

  • To design a novel observation function for runtime multiagent systems.
  • To propose a macroscopic measure for observing system behavioral characteristics.
  • To develop a method for detecting undesirable changes in multiagent systems.

Main Methods:

  • Theoretical analysis of a macroscopic behavioral model for multiagent systems.
  • Design of a macroscopic measure based on the variance of fluctuation of an activity factor.
  • Experimental validation using a test bed system.

Main Results:

  • The proposed measure demonstrates rapid reaction to system changes.
  • A drastic increase in the measure indicates abnormal system behavior.
  • Experimental results confirm the measure's effectiveness in detecting undesirable changes.

Conclusions:

  • The proposed macroscopic measure is effective for observing runtime multiagent system behavior.
  • This measure can be utilized for early detection of anomalies and negative effects.
  • The findings contribute to improved management and control of dynamic multiagent systems.