You should really look into the emerging field of probabilistic programming. Avi Pfeffer has a nice book out on it, Practical Probabilistic Programming (or at least, you can get PDFs by pre-ordering). It basically expands the PGM way of reasoning to Turing-complete domains, and "hides" the problem of coding custom inference algorithms by making them parts of the language runtime.
My personal prediction is that once we get good at learning whole probabilistic programs from data rather than just inferring free numerical parameters from data, this is going to become the dominant mode of machine reasoning.
My personal prediction is that once we get good at learning whole probabilistic programs from data rather than just inferring free numerical parameters from data, this is going to become the dominant mode of machine reasoning.