MAXWELL (Model Annotation eXtension With Exploratory Label Learning)

Team AIS proposes Model Annotation eXtension With Exploratory Label Learning (MAXWELL), an applied research effort to develop a semi-automated framework to annotate simulation models for increased utility and efficiency of model composition. MAXWELL will achieve this by combining assistive Natural Language Processing (NLP), automated model modification and behavior analysis, and a guided semi-automatic analyst annotation workflow. Simulation models may come from multiple platforms and the model assets available for analysis and information extraction may vary wildly. MAXWELL will overcome the challenges associated with diverse information sources by structuring each model’s unique attributes into a universal model annotation formalism, allowing the analyst to use semantic queries to discover all types of models from various frameworks. This conversion will be performed via automating the extraction of information and annotation as much as possible while keeping analysts in the loop, enabling them to make additional annotations and corrections that inform automation improvements. MAXWELL will efficiently define the attributes of a model in an extensible, integrable model annotation formalism that concentrates on model aspects relevant to semantic understanding and composability while respecting security classification. These attributes will aid in identifying suitable existing models and determining required modifications and compositions to meet a simulation requirement.