May I sit in on the meeting as a fly on the wall?
If so, when/how shall I connect?
Thanks in advance!
Douglas Miles
On Thu, Apr 1, 2021 at 2:52 AM Michele Thiella <[email protected]
<mailto:[email protected]>> wrote:
Hi Nil,
you're right! currently EET corresponds to the Italian time!
Great, then I might be a few minutes late because I have a lesson
first. But surely 10.45am EET can work!
Also for me, no problems for those who want to join!
Thanks for the PLN link. See you tomorrow.
Michele
Il giorno mercoledì 31 marzo 2021 alle 14:40:38 UTC+2 Nil ha scritto:
Hi Michele,
On 3/27/21 12:12 PM, Michele Thiella wrote:
> Is there any recommended book/paper to study before the code
of PLN rules?
Search for Probabilistic Logic Networks in
https://wiki.opencog.org/w/Background_Publications#Books_Directly_Related_to_OpenCog_AI
<https://wiki.opencog.org/w/Background_Publications#Books_Directly_Related_to_OpenCog_AI>
> For the meeting, could it be at 11.30am EET?
11:30am EET works for me. But maybe you mean 10:30am EET. With
daylight saving time it seems EET corresponds to Italy time. I'm
not
sure so double check but anyway 10:30am Italy time works for me.
Nil
>
> Michele
>
> Il giorno venerdì 26 marzo 2021 alle 08:56:11 UTC+1 Nil ha
scritto:
>
> On 3/25/21 9:03 PM, Michele Thiella wrote:
> > Can I ask you to say something about tree of decisions in Eva?
> Was it a
> > separate scheme/python module that analyzed SequentialAnd?
> > While i'm at it, I can't place some components in your
architecture:
> > I read Moshe Looks thesis on MOSES and what I found on
OpenPsi.
> But in
> > practice what were they used for?
>
> MOSES is a program learner. In principle it could learn any
program, in
> practice it is mostly used to learn multivariable boolean
functions (as
> it doesn't work very well on anything else, so far anyway).
>
> See for more info
>
>
https://wiki.opencog.org/w/Meta-Optimizing_Semantic_Evolutionary_Search
<https://wiki.opencog.org/w/Meta-Optimizing_Semantic_Evolutionary_Search>
>
<https://wiki.opencog.org/w/Meta-Optimizing_Semantic_Evolutionary_Search
<https://wiki.opencog.org/w/Meta-Optimizing_Semantic_Evolutionary_Search>>
>
>
> > Finally, in practice what does PLN do/have more than URE?
>
> The URE is a generic rewriting system, that needs a rule set to
> operate.
>
> See for more info
>
> https://wiki.opencog.org/w/Unified_rule_engine
<https://wiki.opencog.org/w/Unified_rule_engine>
> <https://wiki.opencog.org/w/Unified_rule_engine
<https://wiki.opencog.org/w/Unified_rule_engine>>
>
> Such rule set can be PLN, which has been specifically
tailored to
> handle
> uncertain reasoning
>
> https://github.com/opencog/pln
<https://github.com/opencog/pln> <https://github.com/opencog/pln
<https://github.com/opencog/pln>>
>
> or the Miner, which is has been tailored to find frequent
subgraphs
>
> https://github.com/opencog/miner
<https://github.com/opencog/miner>
<https://github.com/opencog/miner
<https://github.com/opencog/miner>>
>
> or more, though these are the two most used/mature.
>
> Nil
>
> >
> >
> > Before reasoning is possible, one must have a world-model.
This
> > model has several parts to it:
> > * The people in the room, and their 3D coordinates
> > * The objects on the table and their 3D coordinates.
> > * The self-model (current position of robot, and of its
arms, etc.)
> > The above is updated rapidly, by sensor information.
> >
> > Then there is some long-term knowledge:
> > * The names of everyone who is known. A dictionary linking
names to
> > faces.
> >
> > Then there is some common-sense knowledge:
> > * you can talk to people,
> > * you can pick up bottles on a table
> > * you cannot talk to bottles
> > * you cannot pick up people.
> > * bottles can be picked up with the arm.
> > * facial expressions and arm movements can be used to
communicate
> > with people.
> >
> > The world model needs to represent all of this. It also
needs to
> > store all of the above in a representation that is
accessible to
> > natural language, so that it can talk about the position of
its arm,
> > the location of the bottle, and the name of the person it is
> talking to.
> >
> > Reasoning is possible only *after* all of the above has been
> > satisfied, not before. Attempts to do reasoning before the
above
> > has been built will always come up short, because some
important
> > piece of information will be missing, or will be stored
somewhere,
> > in some format that the reasoning system does not have
access to it.
> >
> > The point here is that people have been building "reasoning
systems"
> > for the last 30 or 40 years. They are always frail and
fragile. They
> > are always missing key information. I think it is
important to try
> > to understand how to represent information in a uniform
manner, so
> > that reasoning does not stumble.
> >
> >
> > Atomspace:
> >
> > Concepts: "name" - "3D pose"
> > - bottle - Na
> > - table - Na
> > (Predicate: "over" List ("bottle") ("table"))
> > Actions:
> > - Go random
> > - Go to coord
> > - Grab obj
> >
> > Goal: (bottle in hand) // = grab bottle
> >
> > Inference rules: all the necessary rules, i.e.
> > * grab-rule: preconditions: (robot-coord = obj-coord) ...,
> > effects: (obj in hand) ...
> > * coord-rule: if x is in "coord1" and y is over x then y is in
> > "coord1"
> >
> > -> So, robot try backward chaining to find the behavior
tree to
> > run. It doesn't find it, it lacks knowledge, it doesn't know
> > where the bottle is (let's leave out partial trees).
> > -> Go random ...
> > -> Vision sensor recognizes table
> > -> atomspace update: table in coord (1,1,1)
> > -> forward chaining -> bottle in coord (1,1,1)
> > -> backward chaining finds a tree, that is
> > Go to coord (1,1,1) + Grap obj
> > -> goal achieved
> >
> >
> > This is a more-or-less textbook robotics homework
assignment. It has
> > certainly been solved in many different ways by many different
> > people using many different technologies, over the last
40-60 years.
> > Algorithms like A-star search are one of the research
results of
> > trying to solve the above. The AtomSpace would be a horrible
> > technology to solve the above problem, its too slow, too
bulky, too
> > complicated.
> >
> > The chaining steps can be called "inference", but it is
inference
> > devoid of natural language, devoid of "true understanding".
My goal
> > is to have a conversation with the robot:
> >
> > "What do you see?"
> > "A bottle"
> > "where is it?"
> > "on the table"
> > "can you reach it?"
> > "no"
> > "could you reach it if you move to a different place?"
> > "yes"
> > "where would you move?"
> > "closer to the bottle"
> > "can you please move closer to the bottle?"
> > (robot moves)
> >
> >
> > This is now clear to me, but why natural language?
> > if i didn't want interactions with humans could i do it
differently?
> > A certain variation of the sensor values already represents
"the
> forward
> > movement", I do not need to associate a name with it if I
don't
> speak,
> > also for the Atom "bottle" I could use its ID instead.
> > I don't understand why removing natural language implies
having an
> > inference devoid of "true understanding".
> >
> > Stupid example: If I speak Italian with a French, neither
of us
> > understands the other. But a bottle remains a bottle for
both and
> if I
> > give him my hand he will probably do it too ... or he will
leave
> without
> > saying goodbye.
> >
> > I'm probably missing something big, but until I don't bang
my head
> > against it, I don't see.
> >
> >
> > This can be solved by carefully hand-crafting a chatbot
dialog tree.
> > (The ghost chatbot system in opencog was designed to allow
such
> > dialog trees to be created) Over the decades, many chatbots
have
> > been written. Again: there are common problems:
> >
> > -- the text is hard-coded, and not linguistic. Minor
changes in
> > wording cause the chatbot to get confused.
> > -- there is no world-model, or it is ad hoc and scattered
over many
> > places
> > -- no ability to perform reasoning
> > -- no memory of the dialog ("what were we talking about?" -
well,
> > chatbots do have a one-word "topic" variable, so the
chatbot can
> > answer "we are talking about baseball", but that's it.
There is no
> > "world model" of the conversation, and no "world model" of
who the
> > conversation was with ("On Sunday, I talked to John about a
bottle
> > on a table and how to grasp it")
> >
> > Note that ghost has all of the above problems. It's not
linguistic,
> > it has no world-model, it has no defined representation
that can be
> > reasoned over, and it has no memory.
> >
> > 20 years ago, it was hard to build a robot that could grasp a
> > bottle. It was hard to create a good chatbot.
> >
> > What is the state of the art, today? Well, Tesla has
self-driving
> > cars, and Amazon and Apple have chatbots that are very
> > sophisticated. There is no open source for any of this,
and there
> > are no open standards, so if you are a university grad
student (or a
> > university professor) it is still very very hard to build a
robot
> > that can grasp a bottle, or a robot that you can talk to.
And yet,
> > these basic tasks have become "engineering"; they are no
longer
> > "science". The science resides at a more abstract level.
> >
> > --linas
> >
> >
> > I find the abstract level incredible, both in terms of
beauty and
> > difficulty!
> >
> > Michele
> >
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