  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.
