  Probabilistic programming is considered as a framework, in which basic
components of cognitive architectures can be represented in unified and elegant
fashion. At the same time, necessity of adopting some component of cognitive
architectures for extending capabilities of probabilistic programming languages
is pointed out. In particular, implicit specification of generative models via
declaration of concepts and links between them is proposed, and usefulness of
declarative knowledge for achieving efficient inference is briefly discussed.
