IIRC these nets don't actually build a knowledge base (though I may be wrong, it's been a while). They're still functioning in lossy continuous space. This is

IIRC these nets don't actually build a knowledge base (though I may be wrong, it's been a while). They're still functioning in lossy continuous space. This is probably fine with a low number of symbols and relations, but once you get to the level of a neural theorem prover you need to properly handle symbol binding. If I'm working with integers and integer-valued variables, how do I represent the variables? How do I represent the operations? This gets more complex once you leave a closed domain, where all the operations and variables are defined up front, and you enter an open domain like theorem proving. There are definitely good applications for small-scale symbolic work; you could possibly improve efficiency of warehouse robots, for example. Highly (pro/ef)ficient neural theorem provers would be amazingly useful, though. Not to mention reasoning over medical knowledge given only the source documents (journal articles). In either of the two domains above you need to extract symbols and relations directly from text. I think symbol-identification and relation-identification, for unstructured documents, can likely be done with some of the state-of-the-art models (particularly thinking of Diffbot[0] here). So use something like Diffbot to identify symbols+relations, then... how do you represent them? Once you have the symbols you can use traditional reasoning tech on them, but it's going to be extremely slow for any reasonable unit of work. Which is exactly why you'd want to piggyback off of neural work. --- Some work has been done on neural theorem proving (NTP). See [1][2]. There are justifications for replacing the existing graph search vs. providing better search heuristics, but I'm fuzzy on the details. Either approach should be eligible for reinforcement learning. [0]: https://www.diffbot.com/products/natural-language/ https://www.diffbot.com/products/natural-language/ [1]: https://arxiv.org/abs/1705.11040 https://arxiv.org/abs/1705.11040 [2]: https://arxiv.org/abs/1606.04442 https://arxiv.org/abs/1606.04442 (This is just for premise selection; it doesn't replace the prover machinery) [3]: https://arxiv.org/abs/1701.06972 https://arxiv.org/abs/1701.06972 (More guided search)