  We introduce Deep Neural Programs (DNP), a novel programming paradigm for
writing adaptive controllers for cy-ber-physical systems (CPS). DNP replace if
and while statements, whose discontinuity is responsible for undecidability in
CPS analysis, intractability in CPS design, and frailness in CPS
implementation, with their smooth, neural nif and nwhile counterparts. This not
only makes CPS analysis decidable and CPS design tractable, but also allows to
write robust and adaptive CPS code. In DNP the connection between the sigmoidal
guards of the nif and nwhile statements has to be given as a Gaussian Bayesian
network, which reflects the partial knowledge, the CPS program has about its
environment. To the best of our knowledge, DNP are the first approach linking
neural networks to programs, in a way that makes explicit the meaning of the
network. In order to prove and validate the usefulness of DNP, we use them to
write and learn an adaptive CPS controller for the parallel parking of the
Pioneer rovers available in our CPS lab.
