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arXiv Machine Learning By Jimin Choi, Max Z. Li

Bayesian Ambiguity Contraction-based Adaptive Robust Markov Decision Processes for Adversarial Surveillance Missions

Read the original on arXiv Machine Learning →

The paper introduces an adaptive Robust Markov Decision Process framework for autonomous Intelligence, Surveillance, and Reconnaissance missions using Collaborative Combat Aircraft. It models adversarial uncertainty with finite threat prototypes and refines policies by eliminating inconsistent threat models, enabling a shift from worst‑case protection to more efficient mission strategies while preserving robustness. Experiments demonstrate higher mission rewards and fewer exposure events compared to nominal and static robust planners across various network topologies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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