John D. Lowrance

Template-Based Structured Argumentation

A semiautomated approach to evidential reasoning uses template-based structured argumentation. A template captures best analytic practice as a hierarchically structured set of coordinated questions; an argument answers the questions posed by a template, including references to the source material used as evidence to support those answers. Graphical depictions of arguments readily convey lines of reasoning, from evidence through to conclusions, making it easy to compare and contrast alternative lines of reasoning. Collaborative analysis is supported via simultaneous access to arguments through web browser clients connected to a common argument server. This approach to analysis has been applied to a wide range of analytic problems and has been experimentally shown to speed the development and improve the quality of analytic assessments.

Multiple-target tracking in dense noisy environments: a probabilistic mapping perspective

A new approach is taken to address the various aspects of the multiple- target tracking (MTT) problem in dense and noisy environments. Instead of fixing the trackers on the potential targets as the convention tracking algorithms do, this new approach is fundamentally different in that an array of parallel-distributed trackers is laid in the search space. The difficult data-track association problem that has challenged the conventional trackers becomes a nonissue with this new approach. By partitioning the search space into cells, this new approach, called PMAP (probabilistic mapping), dynamically calculates the spatial probability distribution of targets in the search space via Bayesian updates. The distribution is spread at each time step, following a fairly general Markov-chain target motion model, to become the prior probabilities of the next scan. This framework can effectively handle data from multiple sensors and incorporate contextual information, such as terrain and weather, by performing a form of evidential reasoning. Used as a pre- filtering device, the PMAP is shown to remove noiselike false alarms effectively, while keeping the target dropout rate very low. This gives the downstream track linker a much easier job to perform. A related benefit is that with PMAP it is now possible to lower the detection threshold and to enjoy high probability of detection and low probability of false alarm at the same time, thereby improving overall tracking performance. The feasibility of using PMAP to track specific targets in an end-game scenario is also discussed. Both real and simulated data are used to illustrate the PMAP performance. Some related applications based on the PMAP approach, including a spatial-temporal sensor data fusion application, are mentioned.

Toward culturally informed option awareness for influence operations with S-CAT

The Socio-Cultural Analysis Tool (S-CAT) is being developed to help decision makers better understand the plausible effects of actions taken in situations where the impact of culture is both significant and subtle. We describe S-CAT in the context of a hypothetical influence operation that serves as an illustrative use case. One of the many challenges in developing SCAT involves providing transparency into the model. S-CAT does this by providing explanations of the analysis it provides. This paper describes how S-CAT can improve option-awareness during influence operations and discusses the explanation capabilities used by S-CAT to support transparency into the model.

Advanced patterns and matches in link analysis

The Link Analysis Workbench (LAW) is a tool for detecting and monitoring situations of interest using inexact matching of graphical patterns. Here we describe some recent advances to LAW: incorporating hierarchy, cardinality, disjunction, and constraints in the pattern language and similarity metric, and a flexible, user-friendly interface for displaying matching data. These capabilities support analysts in rapidly exploring and understanding large, incomplete relational data sets.