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Generative Policy-based Models aim to enable a coalition of systems, be theydevices or services to adapt according to contextual changes such asenvironmental factors, user preferences and different tasks whilst adhering tovarious constraints and regulations as directed by a managing party or thecollective vision of the coalition. Recent developments have proposed newarchitectures to realize the potential of GPMs but as the complexity of systemsand their associated requirements increases, there is an emerging requirementto have scenarios and associated datasets to realistically evaluate GPMs withrespect to the properties of the operating environment, be it the futurebattlespace or an autonomous organization. In order to address thisrequirement, in this paper, we present a method of applying an agile knowledgerepresentation framework to model requirements, both individualistic andcollective that enables synthetic generation of ground truth data such thatadvanced GPMs can be evaluated robustly in complex environments. We alsorelease conceptual models, annotated datasets, as well as means to extend thedata generation approach so that similar datasets can be developed for varyingcomplexities and different situations.

Synthetic Ground Truth Generation for Evaluating Generative Policy Models