Mobile Target Search with Imperfect Perception: A Partially Observable Stochastic Game Theoretical Approach
- URL: http://arxiv.org/abs/2606.20232v1
- Date: Thu, 18 Jun 2026 13:44:12 GMT
- Title: Mobile Target Search with Imperfect Perception: A Partially Observable Stochastic Game Theoretical Approach
- Abstract summary: This paper investigates mobile target search under imperfect perceptions caused by sensor limitations, malicious jamming, or communication noise.<n>We adopt the partially observable dynamic game (POSG) approach, which generalizes partially observable decision processes.<n>We develop a server-assisted distributed algorithm that utilizes the aggregative potential game structure for searchers and a KL-divergence-based reduction for target prediction.
- Score: 0.688204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper investigates mobile target search under imperfect perceptions caused by sensor limitations, malicious jamming, or communication noise. Searchers and targets operate in a grid-shaped area with bounded mobility, leading to a dynamic interplay between search and evasion. To capture this adversarial interaction under imperfect perceptions, we adopt the partially observable stochastic game (POSG) approach, which generalizes partially observable Markov decision processes (POMDPs) by incorporating target intelligence. To handle false alarms and missed detections caused by perceptual uncertainties, we propose a novel detectability concept to determine whether a search strategy guarantees eventual detection, and provide sufficient detectability criteria based on stochastic recurrence analysis. We further develop a server-assisted distributed algorithm that utilizes the aggregative potential game structure for searchers and a KL-divergence-based reduction for target prediction. Numerical simulations validate the effectiveness of the proposed algorithm and support the detectability analysis.
Related papers
- Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks [10.312968200748115]
We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems.<n>We propose a threshold-based probing strategy and show its effectiveness via a simplified partially observable Markov decision process (POMDP) formulation.
arXiv Detail & Related papers (2026-04-13T12:54:24Z) - Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning [49.28548464288051]
Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text.<n>In intrinsic noise in CIR triplets incurs intrinsic uncertainty and threatens the model's robustness.<n>This paper introduces a Heterogeneous Uncertainty-Guided (HUG) paradigm to overcome these limitations.
arXiv Detail & Related papers (2026-01-16T16:05:49Z) - On the Adversarial Robustness of Learning-based Conformal Novelty Detection [10.58528988397402]
We study the adversarial robustness of conformal novelty detection using AdaDetect.<n>Our results show that adversarial perturbations can significantly increase the FDR while maintaining high detection power.
arXiv Detail & Related papers (2025-10-01T03:29:11Z) - An Uncertainty-aware DETR Enhancement Framework for Object Detection [10.102900613370817]
We propose an uncertainty-aware enhancement framework for DETR-based object detectors.<n>We derive a Bayes Risk formulation to filter high-risk information and improve detection reliability.<n> Experiments on the COCO benchmark show that our method can be effectively integrated into existing DETR variants.
arXiv Detail & Related papers (2025-07-20T07:53:04Z) - Token-Level Adversarial Prompt Detection Based on Perplexity Measures
and Contextual Information [67.78183175605761]
Large Language Models are susceptible to adversarial prompt attacks.
This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs.
We introduce a novel approach to detecting adversarial prompts at a token level.
arXiv Detail & Related papers (2023-11-20T03:17:21Z) - Small Object Detection via Coarse-to-fine Proposal Generation and
Imitation Learning [52.06176253457522]
We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning.
CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
arXiv Detail & Related papers (2023-08-18T13:13:09Z) - Object Detection as Probabilistic Set Prediction [3.7599363231894176]
We present a proper scoring rule for evaluating and training probabilistic object detectors.
Our results indicate that the training of existing detectors is optimized toward non-probabilistic metrics.
arXiv Detail & Related papers (2022-03-15T15:13:52Z) - Multi-Agent Active Search using Detection and Location Uncertainty [6.587280549237275]
Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty.
We first propose an inference method to jointly handle both target detection and location uncertainty.
We then build a decision making algorithm that uses Thompson sampling to enable decentralized multi-agent active search.
arXiv Detail & Related papers (2022-03-09T04:53:37Z) - Learning to Separate Clusters of Adversarial Representations for Robust
Adversarial Detection [50.03939695025513]
We propose a new probabilistic adversarial detector motivated by a recently introduced non-robust feature.
In this paper, we consider the non-robust features as a common property of adversarial examples, and we deduce it is possible to find a cluster in representation space corresponding to the property.
This idea leads us to probability estimate distribution of adversarial representations in a separate cluster, and leverage the distribution for a likelihood based adversarial detector.
arXiv Detail & Related papers (2020-12-07T07:21:18Z) - Bayesian Optimization with Machine Learning Algorithms Towards Anomaly
Detection [66.05992706105224]
In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique.
The performance of the considered algorithms is evaluated using the ISCX 2012 dataset.
Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
arXiv Detail & Related papers (2020-08-05T19:29:35Z) - UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional
Variational Autoencoders [81.5490760424213]
We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
arXiv Detail & Related papers (2020-04-13T04:12:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.