Proceedings

Belief-Contraction-Driven Active Inverse Source Localization and Characterization

Yiwei Shi, Mengyue Yang, Qi Zhang, Cunjia Liu, Weinan Zhang, Weiru Liu

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI4Tech: AI Enabling Technologies. Pages 6528-6535.

https://doi.org/10.24963/ijcai.2026/726

Keywords

  • Advanced AI4Tech: Deep AI4Tech
  • Advanced AI4Tech: AI4Tech foundations
  • Advanced AI4Tech: Data-driven AI4Tech

Abstract

Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, where a mobile sensor must infer latent source parameters from sparse, noisy readings. We introduce a belief-contraction-driven approach that unifies inference, stopping, and control. An attention-augmented particle filter stabilizes Bayesian belief updates through ESS-based resampling, feature-aware sparse attention smoothing, and Metropolis–Hastings rejuvenation that preserves the filtering posterior. Belief contraction (posterior dispersion) defines both a termination rule and a goal-aligned intrinsic reward, enabling reinforcement learning without distance-to-source shaping. Across seven field modalities, spatial out-of-distribution tests, and nonstationary source shifts, our agent (ATT-PFRL) achieves higher completion, faster convergence, and more accurate localization than planning and RL+Bayes baselines under similar computation. Fixed-trajectory studies also show improved ESS and lower RMSE, isolating the benefit of the inference layer.

Yiwei Shi, Mengyue Yang, Qi Zhang, Cunjia Liu, Weinan Zhang, and Weiru Liu. 2026. Belief-Contraction-Driven Active Inverse Source Localization and Characterization. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 6528–6535. https://doi.org/10.24963/ijcai.2026/726
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