|
|
|
|
|
by JohnStrange
3571 days ago
|
|
It's a pretty cool idea but nothing groundbreaking. There is plenty of AI-driven mathematics already, e.g. the Mizar theorem prover. Pollock used to advocate the idea of continuously trying to prove theorems that might be useful later. It's an underrated idea, in my opinion, and his system of defeasible reasoning based on graph theory is still one of the best. What's new in this paper is the probabilistic component, trying to guess the outcome of complicated proofs. That's a neat idea, but nothing revolutionary. It may give rise to nice shortcuts for better efficiency. The real problem is making the machine get a good hunch what to prove, so it doesn't find useful theorems just randomly. I'm not working in this field, so correct me if I'm wrong, but that seems to be rather hard. In any case, as far as I know most automated theorem provers are only semi-automatic, you have to give them an idea about which direction to go and which proof strategy to use. |
|
I'm working in exactly this area, and it's very nice to see it mentioned occasionally as a useful direction!
There's a bunch of nice work being done on this problem; I'm mostly familiar with (roughly chronologically) IsaScheme, IsaCoSy, QuickSpec, Hipspec and Hipster. These take in a bunch of function definitions and output equations about them; they work by enumerating (type-correct) terms and using random testing (QuickCheck) to quickly separate unequal terms from each other, then they apply automated theorem provers to the remainder.
There are also more first-order, less computationally-focused systems for generating theorems out there, like HR and Graffiti.