Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing

TL;DR

This paper presents a novel abductive approach for generating realistic human movement trajectories that meet location and time constraints, with explainability and scalability, tested in government scenarios.

Contribution

Introduces a new abductive method guided by a parsimony function for efficient, explainable trajectory generation scalable to large scenarios.

Findings

Method produces exact, realistic trajectories.

Scales effectively to large, complex scenarios.

Trajectories can evade machine learning anomaly detection.

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.