Fast Neural-Network Approximation of Active Target Search Under Uncertainty

2026-04-24

AI Summary

This work addresses the challenge of efficiently searching for stationary targets whose number and locations are unknown, under observation uncertainty. The authors propose the first convolutional neural network–based policy that directly approximates the decision-making process of Active Search and its intermittent variant, substantially reducing online computational overhead. The approach integrates a probability hypothesis density (PHD) filter for target state estimation and trains the network using a multi-channel grid input encoding target belief, agent position, visitation history, and boundary information. Simulations demonstrate that the proposed method achieves detection performance comparable to Active Search across both uniform and clustered target distributions while offering computational speedups of several orders of magnitude.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Active Learning
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Economics, Online Markets and Human Computation: Social networks and social learning

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.

Problem

• active target search
• uncertainty
• mobile agent
• online optimization
• target detection

Innovation

• convolutional neural network
• active search
• probability hypothesis density filter
• online optimization
• target detection