On 04/28/2017 06:49 PM, Linas Vepstas wrote:
On Fri, Apr 28, 2017 at 1:34 AM, Nageen Naeem <[email protected]
<mailto:[email protected]>> wrote:
is opencog knowledge representation language is able to learn things?
Yes, but that is a topic of current active research. There are three
ways to do this:
1) use moses
2) use the pattern miner
3) use the language-learning subsystem.
4) the neural net subsystem, Ralf is working on that, its a kind-of
generalization of the earlier "destin", and using tensorflow under the
covers. So far, it's been used to create facial expressions (for use in
humanoid robots)
Reasoning can be used too , you could for instance query
Implication
<go-to-the-store>
Variable "$X"
via the backward chainer and it would fill the blanks with $X that
directly and indirectly match. That is an inefficient form of learning,
but still.
Nil
I'm currently am working on language learning and have vague plans to
port it over to the pattern miner, someday. I haven't looked at the
pattern miner yet, I'm guessing that it remains at a rather primitive,
low level, for now.
Basically, moses is "mature" the other three are not, they're in very
active development.
--linas
On Friday, April 28, 2017 at 9:47:45 AM UTC+5, Daniel Gross wrote:
Hi Linas,
I guess i should further ask:
What determines the expressiveness of OpenCogs representation, the
one that is bult-into its inference.
thank you,
Daniel
On Thursday, 27 April 2017 05:27:45 UTC+3, linas wrote:
On Wed, Apr 26, 2017 at 2:06 PM, Nageen Naeem
<[email protected]> wrote:
how I can differentiate knowledge representation in OpenCog
and traditional knowledge representation techniques.
Opencog is really pretty traditional in its representation form.
There are whizzy bits: the ability to assign arbitrary
valuations to the KR (e.g. floating point probabilities). Maybe
I should say that opencog allows you to "design your own KR",
although it provides a reasonable one, based on the PLN books.
There's a pile of tools not available in other KR systems,
including a sophisticate pattern matcher, a prototype pattern
miner, a learning subsystem, an NLP subsystem. Its an active
project, its open source, with these last two distinguishing it
from pretty much everything else.
--linas
On Thursday, April 27, 2017 at 12:02:16 AM UTC+5, Nageen
Naeem wrote:
basically, i want to compare knowledge representation
techniques, want to compare knowledge representation in
OpenCog and in clarion? any description, please.
On Wednesday, April 26, 2017 at 11:54:11 PM UTC+5, linas
wrote:
On Wed, Apr 26, 2017 at 1:41 PM, Nageen Naeem
<[email protected]> wrote:
OpenCog didn't shift to java from c++?
You are welcome to study https://github.com/opencog
for the source languages used.
Thanks for defining pros and cons if there is
any paper on comparison with other architecture
kindly recommend me.
Ben has written multiple books on the archtiecture
in general. The wiki describes particular choices.
I am not aware of any other
(knowledge-representation) architectures that can do
what the atomspace can do. So I'm not sure what you
want to compare against. Triplestore? various
actionscripts? Prolog?
--linas
On Wednesday, April 26, 2017 at 9:36:04 PM
UTC+5, Ben Goertzel wrote:
OpenCog did not shift from Java to C++, it
was always C++
The advantage of Atomspace is that it allows
fine-grained semantic
representations of all forms of knowledge in
a common framework. The
disadvantage is, this makes things
complicated. The other advantage
is, this fine-grained representation makes
data amenable to multiple
AI algorithms, including ones that can work
together synergetically
ben
On Wed, Apr 26, 2017 at 12:10 PM, Nageen
Naeem <[email protected]> wrote:
> Hey,
> I'm searching for pros and cons for using
atomspace for knowledge
> representation but didn't get any full-fledged
answer related to it. what
> are the pros and cons of using atomspace and why
OpenCog shifted to java
> from c++ what are reasons behind it?
>
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