I think the neural part comes from the fact that it use an (hyper) graph of
probabilities like explained by Matt. The symbolic part stems from the fact
that nodes in the graph are labeled which strings which form a programming
language that can take advantage of FOL. My understanding is that atomspace
is the paper on which you write AGI programs. A regular programming
language is coded in text file, OpenCog is coded in atomspace. Or maybe I
am misled. I am just a lurker too.

On Thu, May 4, 2017 at 8:20 PM Matt Chapman <[email protected]> wrote:

> It's a weighted, labeled hypergraph, where a "symbol" can be represented
> by a single node or a network-of-nodes/sub-graph. Ideally, Inference and
> Hebbian Learning rules allow new "symbols" to form automatically as new
> nodes are created to connect groups of related nodes. The weights of the
> nodes are probabilistic truth values.
>
> That's how I know to summarize it; hope it helps. Beware, I'm a hobbyist
> lurker, not active on the project currently, and I may be completely wrong
> or significantly misleading, with significant non-zero probability. ;-)
> Read the wiki for further details and possibly more precision, though
> beware the possibility of vastly outdated content, so the real
> source-of-truth is the code on github.
>
> All the Best,
>
> Matt
>
> --
> Standard Disclaimer:
> Please interpret brevity as me valuing your time, and not as any negative
> intention.
>
> On Wed, May 3, 2017 at 1:36 AM, Vishnu Priya <[email protected]>
> wrote:
>
>> Hello All,
>>
>>  i read that, Atomspace is a neural-symbolic network. How?
>> or is it only a weighted hypergraph? Could any one please explain?
>>
>> Thanks,
>> Vishnu
>>
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