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Game-theoretic planning for multiplayer defense task with online objective function parameter estimation.

Hongwei Fang1, Peng Yi2

  • 1Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China.

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|January 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a game-theoretic path planning algorithm for intrusion-defense scenarios. It enables defenders to estimate intruder behavior and cooperatively plan interception strategies for enhanced security.

Keywords:
Iterative best responseMultiplayer intrusion-defense gameReceding horizon optimizationTask-assignmentUnscented Kalman filter

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Area of Science:

  • Robotics and Control Systems
  • Game Theory
  • Cybersecurity

Background:

  • Intrusion-defense games present complex challenges due to dynamic environments and adversarial interactions.
  • Cooperative strategies among defenders are crucial for effective area protection.
  • Real-time adaptation to unknown adversary objectives is a significant hurdle in defense systems.

Purpose of the Study:

  • To develop a game-theoretic path planning algorithm for multiplayer intrusion-defense games.
  • To enable defenders to estimate unknown intruder objective function parameters in real-time.
  • To facilitate cooperative Nash equilibrium computation among defenders for optimal interception strategies.

Main Methods:

  • Formulation of the intrusion-defense game using a receding horizon approach.
  • Design of coupled objective functions considering predicted defender-intruder interactions.
  • Implementation of a distributed proximal iterative best response scheme for cooperative Nash equilibrium.
  • Integration of an unscented Kalman filter for online estimation of intruder parameters.

Main Results:

  • The proposed algorithm effectively assigns defenders to intruders via integer optimization.
  • The distributed scheme allows defenders to cooperatively compute Nash equilibrium.
  • Online estimation of intruder parameters enhances the adaptability of defense strategies.
  • Simulation experiments validate the proposed method's effectiveness in intrusion-defense scenarios.

Conclusions:

  • The developed game-theoretic path planning algorithm enhances cooperative defense capabilities.
  • Online parameter estimation significantly improves the system's response to unknown adversary objectives.
  • The approach offers a robust solution for complex multiplayer intrusion-defense scenarios.