| Abstract |
To facilitate information sharing between systems and devices in a coalition operation, unstructured data from various sensors must be analysed accordingly. Recent work has developed the notion of a context-dependant generative policy framework capable of learning generative policy models from strings and text-based data in a tabular format. However, in order to fully utilise generative policy models in coalition environments, it is vital that unstructured contextual information can be analysed alongside tabular data, potentially at the edge of the network. This paper performs a deep-dive into the emerging field of neural-symbolic machine learning with a view towards enabling future neural-symbolic generative policy models that are capable of analysing both structured and unstructured data present in a coalition operation, whilst providing full transparency and enabling edge of network reasoning capability. Specifically, a technique called DeepProbLog is investigated and applied to a coalition scenario, highlighting key research challenges and questions to be addressed in order to advance the capability of generative policy models for coalition operations. |