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
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
Such rule set can be PLN, which has been specifically tailored to handle
uncertain reasoning
https://github.com/opencog/pln
or the Miner, which is has been tailored to find frequent subgraphs
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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