> As for intuitionistic logic, I was interested in how well the model trained 
> on set.mm would transfer its skills to iset.mm. Also many theorems from 
> iset.mm have their set.mm counterparts, so theoretically you could contribute 
> to iset.mm by trying to prove some existing set.mm results in the 
> intuitionistic setting.

Very interesting. Arguably higher level theorems proofs would
eventually become ~~ similar?

> Basic arithmetic results are interesting since you can easily generate many 
> "theorems" of the form "12 + 29 = 41" and you can even roughly estimate
> the amount of steps such theorems should require. Proving these identities is 
> a purely mechanical task (as far as I remember, Mario even wrote a program to 
> do that), so it's interesting to know whether your model can learn to perform 
> such a feat.

We have and leverage proof generators for a number of these tasks :+1:

> As for Tarskian geometry, results in that section use a relatively small 
> subset of set.mm theorems and have an underlying geometric intuition. In my 
> opinion it's interesting to evaluate if your system performs better on 
> geometry theorems rather than on propositional calculus/set theory results. 
> Also, elementary geometry section has a collection of results with statements 
> but without proofs (marked by "? @" and "? $.", as mentioned by Thierry).

Good to know, thanks for that!

-stan

On Tue, Mar 31, 2020 at 12:27 PM savask <[email protected]> wrote:
>>
>> How large are the quantum logic and intuitionistic logic databases?
>> What would be the main value of applying our proves on them out of
>> curiosity?
>
>
> Quantum logic database is ~560KB, intuitionistic logic database is ~3.7MB. 
> Trying out your prover on the quantum logic would be interesting because of 
> how different it is from usual logic. Also typical proofs in that database 
> seem to be smaller than in set.mm, so perhaps your algorithm has a higher 
> chance of coming up with proofs in quantum logic.
>
> As for intuitionistic logic, I was interested in how well the model trained 
> on set.mm would transfer its skills to iset.mm. Also many theorems from 
> iset.mm have their set.mm counterparts, so theoretically you could contribute 
> to iset.mm by trying to prove some existing set.mm results in the 
> intuitionistic setting.
>
>> Do you believe the community would be interested in an online tool to 
>> discharge goals?
>
>
> I think others are in a better position to comment on this, since I haven't 
> formalized anything in quite a while. Personally, I would really like to see 
> a Metamath Solitare kind of tool which would autosuggest you steps based on 
> your model, just to explore how well it works in pair with a human operator.
>
>> The model is capable to prove some of the prime number theorems, but
>> proofs sizes do grow very fast. I believe we already proved 17 as an
>> example. Out of curiosity, why are you specifically interested in
>> these two classes of theorems?
>
>
> Basic arithmetic results are interesting since you can easily generate many 
> "theorems" of the form "12 + 29 = 41" and you can even roughly estimate
> the amount of steps such theorems should require. Proving these identities is 
> a purely mechanical task (as far as I remember, Mario even wrote a program to 
> do that), so it's interesting to know whether your model can learn to perform 
> such a feat.
>
> As for Tarskian geometry, results in that section use a relatively small 
> subset of set.mm theorems and have an underlying geometric intuition. In my 
> opinion it's interesting to evaluate if your system performs better on 
> geometry theorems rather than on propositional calculus/set theory results. 
> Also, elementary geometry section has a collection of results with statements 
> but without proofs (marked by "? @" and "? $.", as mentioned by Thierry).
>
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