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]>
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
>>
>> > 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>
>> >
>> >
>> > > 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>
>> >
>> > 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>
>> >
>> > or the Miner, which is has been tailored to find frequent subgraphs
>> >
>> > 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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>> > <
>> https://groups.google.com/d/msgid/opencog/5ac81cf1-c4cd-40cd-9438-55d8dc3d95f5n%40googlegroups.com>
>>
>> >
>> > >
>> > <
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>> > <
>> https://groups.google.com/d/msgid/opencog/5ac81cf1-c4cd-40cd-9438-55d8dc3d95f5n%40googlegroups.com?utm_medium=email&utm_source=footer>>.
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