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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