Repeated Deceptive Path Planning against Learnable Observer

📰 ArXiv cs.AI

Learn to deceive learnable observers in path planning by using repeated deceptive path planning techniques to conceal true destinations

advanced Published 11 May 2026

Action Steps

Formulate the Repeated Deceptive Path Planning (RDPP) problem to account for learnable observers
Model the observer's learning process using machine learning algorithms
Develop a path planning algorithm that adapts to the observer's learning
Simulate the RDPP scenario to evaluate the effectiveness of the deceptive path planning strategy
Analyze the trade-off between deception and efficiency in path planning

Who Needs to Know This

Researchers and engineers working on autonomous systems, robotics, and AI can benefit from this knowledge to improve the security and privacy of their systems

Key Insight

💡 RDPP can be used to conceal true destinations from learnable observers by adapting to their learning process

Full Article

Title: Repeated Deceptive Path Planning against Learnable Observer

Abstract:
arXiv:2605.07174v1 Announce Type: new Abstract: We study the problem of deceptive path planning (DPP), where an agent aims to conceal its true destination from external observers. While existing work assumes static, non-learning observers, real-world adversaries-such as in critical goods transportation or military operations-can adapt by learning from historical trajectories. To address this gap, we introduce Repeated Deceptive Path Planning (RDPP), a new formulation that explicitly models learn

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