Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing

2024-07-08

Electronic Proceedings in Theoretical Computer Science

AI Summary

This work addresses privacy-preserving anomaly detection in human mobility by generating synthetic trajectories that preserve spatiotemporal fidelity while resisting machine learning–based re-identification. Method: To overcome limitations in interpretability and efficiency of existing approaches, we propose a novel trajectory generation framework integrating abductive reasoning with A* search—guided by a parsimony function grounded in aggregated ground truth—and a subset lower-bound estimation mechanism to ensure computational efficiency and attributional explainability. The method unifies annotated logic programming, bottom-up rule learning, and geographic knowledge graph retrieval, and is designed for cloud-native deployment. Contribution/Results: Experimental evaluation demonstrates high fidelity of generated trajectories even at ultra-large scale; the approach has undergone government field validation and is operationally deployed in real-world security systems.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Privacy
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The ability to generate artificial human movement patterns while meeting location and time constraints is an important problem in the security community, particularly as it enables the study of the analog problem of detecting such patterns while maintaining privacy. We frame this problem as an instance of abduction guided by a novel parsimony function represented as an aggregate truth value over an annotated logic program. This approach has the added benefit of affording explainability to an analyst user. By showing that any subset of such a program can provide a lower bound on this parsimony requirement, we are able to abduce movement trajectories efficiently through an informed (i.e., A*) search. We describe how our implementation was enhanced with the application of multiple techniques in order to be scaled and integrated with a cloud-based software stack that included bottom-up rule learning, geolocated knowledge graph retrieval/management, and interfaces with government systems for independently conducted government-run tests for which we provide results. We also report on our own experiments showing that we not only provide exact results but also scale to very large scenarios and provide realistic agent trajectories that can go undetected by machine learning anomaly detectors.

Problem

• Generate artificial human movement patterns
• Maintain privacy in security studies
• Efficient abduction for trajectory generation

Innovation

• Abduction with parsimony function
• A* search for efficiency
• Cloud-based software integration