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Single Real Goal, Magnitude-Based Deceptive Path-Planning.

Kai Xu1, Yunxiu Zeng1, Long Qin1

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Entropy (Basel, Switzerland)
|December 8, 2020
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Summary

This study introduces a new method for deceptive path-planning to minimize goal detection. The approach enhances environmental information use and balances path deception with resource constraints for better strategic applications.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Operations Research

Background:

  • Deceptive path-planning aims to conceal an agent's goal from observers, crucial for public security and logistics.
  • Current methods struggle to fully utilize environmental data or balance path deceptivity with resource limitations.

Purpose of the Study:

  • To formalize a deceptive path-planning problem based on probabilistic goal recognition.
  • To develop a flexible and effective method for maximizing and generating deceptive paths.

Main Methods:

  • Formalized a single real goal magnitude-based deceptive path-planning problem.
  • Developed a mixed-integer programming approach for deceptive path maximization and generation.

Main Results:

  • The proposed method effectively utilizes environmental information for deceptive path-planning.
  • Experimental results demonstrate superior performance compared to existing methods.

Conclusions:

  • The developed model provides a computable foundation for advanced deception strategies.
  • The approach broadens the applicability of deceptive path-planning in various real-world scenarios.