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.

翻译：在满足位置和时间约束条件下生成人工人类移动模式的能力是安全领域的一个重要问题，尤其因为它能够在保护隐私的同时研究检测此类模式的类比问题。我们将该问题构建为一种溯因实例，其由一种新颖的简约性函数引导，该函数表示为带注释逻辑程序上的聚合真值。此方法还具有为分析用户提供可解释性的额外优势。通过证明此类程序的任何子集都能为此简约性要求提供下界，我们能够通过一种启发式（即A*）搜索高效地溯因移动轨迹。我们描述了如何通过应用多种技术来增强我们的实现，以便与基于云的软件栈进行集成和扩展，该软件栈包括自底向上的规则学习、地理定位知识图谱检索/管理，以及与政府系统的接口，用于独立进行的政府运行测试，我们为此提供了结果。我们还报告了我们自身的实验，表明我们不仅能够提供精确结果，而且能够扩展到超大规模场景，并生成能够逃逸机器学习异常检测器检测的逼真智能体轨迹。