  Recent advances in artificial intelligence (AI) offer an opportunity for the
adoption of self-driving networks. However, network operators or home-network
users still do not have the right tools to exploit these new advancements in
AI, since they have to rely on low-level languages to specify network policies.
Intent-based networking (IBN) allows operators to specify high-level policies
that dictate how the network should behave without worrying how they are
translated into configuration commands in the network devices. However, the
existing research proposals for IBN fail to exploit the knowledge and feedback
from the network operator to validate or improve the translation of intents. In
this paper, we introduce a novel intent-refinement process that uses machine
learning and feedback from the operator to translate the operator's utterances
into network configurations. Our refinement process uses a sequence-to-sequence
learning model to extract intents from natural language and the feedback from
the operator to improve learning. The key insight of our process is an
intermediate representation that resembles natural language that is suitable to
collect feedback from the operator but is structured enough to facilitate
precise translations. Our prototype interacts with a network operator using
natural language and translates the operator input to the intermediate
representation before translating to SDN rules. Our experimental results show
that our process achieves a correlation coefficient squared (i.e., R-squared)
of 0.99 for a dataset with 5000 entries and the operator feedback significantly
improves the accuracy of our model.
