A reasonable step would be for Nil to send you some real PLN and URE
inference histories and see what your visualizer does with them...

On Mon, Jul 12, 2021, 10:59 AM Ivan V. <[email protected]> wrote:

> I made a small infinity test <http://ocog.atspace.cc/infinite/> too. Each
> parent virtually has an infinite number of children. Rolling ovals around,
> zooming ovals in, zooming ovals out, ... Surely it's not exactly perfect,
> but I could live with it.
>
> pon, 12. srp 2021. u 17:48 Linas Vepstas <[email protected]> napisao
> je:
>
>> Hi Ivan,
>>
>> On Mon, Jul 12, 2021 at 6:00 AM Ivan V. <[email protected]> wrote:
>>
>>>
>>> Thank you for asking, and my thoughts are pretty obvious. As I
>>> understand, URE and PLN are all about proofs, so my thoughts may go in that
>>> direction. Suppose we have a natural deduction proof composition:
>>>
>>>
>>>
>>>
>>>
>>>
>>> *  ---   ---   ---     ---   ---   ---     ---   ---   ---   I     J
>>> K       L     M     N       P     Q     R -----------------
>>> -----------------   -----------------         A                   B
>>>           C-----------------------------------------------------------
>>>                        X*
>>>
>>> You can already see the tree-like composition, but as it may span over a
>>> very wide and tall area, it may be required to represent it within an
>>> on-demand scaling system. This example <http://ocog.atspace.cc/>
>>> roughly shows what I have imagined for proof representation. In the example
>>> you can play with ovals, dragging them around and in or out the central
>>> area, zooming proof parts of the current interest. Notice how it is
>>> possible to represent and navigate nearly infinite length proofs, assuming
>>> enough memory space.
>>>
>>
>> Re: navigating trees: if you don't already know this, then I suggest that
>> you really, really should study hyperbolic rotations aka mobius
>> transformations on the poincare disk. They implement your example.  I
>> recall seeing a demo of this at SIGGRAPH two or three decades ago. As you
>> pan around on the hyperbolic disk, different parts of the graph get
>> magnified at the center. And, like an MC Escher print, the rest of the
>> graph remains compressed at the edges.
>>
>> For scale-free networks, this doesn't work. And from what I can tell,
>> learning really does result in something close to scale-free networks.
>> What this means in practice is that there's one vertex with a million edges
>> coming off of it.  There are two, with half-a-million each. Four, with a
>> quarter-million each, and so on. So almost all vertexes have just a handful
>> of edges connected to them, but as you move around, from vertex to vertex,
>> you bump into these monsters. And you can't really draw them: try drawing a
>> vertex with a thousand edges on your 2Kx2K monitor: most of those edges
>> will be less than one pixel from each-other. It'll be just a big blob.
>>
>> It's important to "eat your own dog-food", as they say, or "smoke your
>> own dope": use your own code to solve actual, real-world problems. This
>> very quickly highlights where all that beautiful theory doesn't quite work
>> out in practice.
>>
>> --linas
>>
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