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cs.LG updates on arXiv.org

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Fast Neural-Network Approximation of Active Target Search...
Bilal Yousuf · 2026-04-27 · via cs.LG updates on arXiv.org

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Abstract:We address the problem of searching for an unknown number of stationary targets at unknown positions with a mobile agent. A probability hypothesis density filter is used to estimate the expected number of targets under measurement uncertainty. Existing planners, such as Active Search (AS) and its Intermittent variant (ASI), achieve accurate detection but require costly online optimization. To reduce online computation, we propose to use a convolutional neural network to approximate AS or ASI decisions through direct inference. The network is trained on AS/ASI data using a multi-channel grid that encodes target beliefs, the agent position, visitation history, and boundary information. Simulations with uniform and clustered target distributions show that the network achieves detection rates comparable to AS or ASI while reducing computation by orders of magnitude.
Subjects: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2604.22254 [cs.LG]
  (or arXiv:2604.22254v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2604.22254

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Lucian Busoniu [view email]
[v1] Fri, 24 Apr 2026 05:56:36 UTC (3,020 KB)