In years past, GOFAI (Good Old Fashioned AI) was heavily based on "rules" and symbolic computation based on rules. Unfortunately, that approach ran into stumbling blocks, and the world moved heavily towards statistical/probabilistic approaches leading to the current wave of interest in "machine learning".
It seems though, that the symbolic/rule-based approach probably still has application. So, could one "learn" rules using a probabilistic rule induction method, and then layer symbolic computation on top? If so, how could the whole process be made truly two-way, so that something "learned" from processing rules, can be fed back into how the system learns rules?