| Abstract |
To facilitate information sharing between systems and devices in a distributed environment, 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, it is vital that unstructured contextual information can be analysed alongside tabular data, potentially at the edge of the network to enable generative policy models to be applied to more complex tasks. This paper performs a deep-dive into the 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, whilst providing full transparency and enabling edge of network reasoning capability. Firstly, an existing technique called DeepProbLog is investigated and applied to a policy inferencing task based on unstructured data and secondly, a recent inductive logic programming technique currently used for learning generative policy models is evaluated with respect to a policy learning task also based on unstructured data. Finally, the results of both tasks are discussed to provide a platform for future research into enabling neural-symbolic generative policy models. |