Repeated Deceptive Path Planning against Learnable Observer

Researchers introduce Repeated Deceptive Path Planning (RDPP), a framework addressing how agents can conceal destinations from learning adversaries who adapt over time. The proposed Deceptive Meta Planning (DeMP) algorithm uses two-level optimization to sustain deception against evolving observers, outperforming existing static-observer approaches while maintaining reasonable path costs.

This research addresses a critical gap in adversarial multi-agent systems by moving beyond the unrealistic assumption that observers remain static. In real-world scenarios—from supply chain security to military logistics—adversaries learn from historical data and adjust their detection models accordingly. The paper demonstrates that existing deceptive path planning methods fail when observers can adapt, creating a practical security vulnerability in systems relying on trajectory obfuscation.

The technical contribution centers on modeling repeated interactions where both agent and observer improve iteratively. DeMP's two-level optimization elegantly separates concerns: episode-level adaptation handles immediate counter-responses to observer updates, while meta-level updates capture broader patterns in how observers evolve their models. This prevents the accumulation of "adaptation lag" that degrades long-term deception performance.

For cybersecurity and autonomous systems, this work has substantial implications. As machine learning becomes increasingly prevalent in threat detection and tracking systems, any security mechanism relying on concealment must account for adversaries that learn. The findings apply broadly across critical infrastructure, autonomous vehicle routing, and privacy-preserving systems. Organizations deploying path obfuscation strategies should recognize that static deception methods will eventually fail against sophisticated learning adversaries.

Future work likely extends these concepts to multi-agent scenarios where multiple deceptive actors compete against coordinated observers. The framework could inform privacy mechanisms in location-based services and adversarial machine learning defenses. Understanding repeated deceptive interactions strengthens both offensive and defensive capabilities in contested domains.

• →Existing deceptive path planning fails against learning observers who adapt their detection models over time.
• →DeMP combines episode-level and meta-level optimization to sustain deception through repeated interactions.
• →The framework prevents accumulation of adaptation lag that degrades long-term deception performance.
• →Results highlight critical importance of modeling learnable adversaries in multi-agent security systems.
• →Research applies directly to critical infrastructure, autonomous systems, and privacy-preserving applications.