Common Sli
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Transcript of Common Sli
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ROADS TO HUMAN LEVEL AI?
biologicalimitate humans. Even neural nets,
work eventually.
engineeringsolve problems the world presents ahead
direct programming, e.g. genetic algorith
use logic, loftier objective. If it reaches level, we will understand intelligence, and so wrobots. Logical AI has partly solved some inevitablems that havent even been noticed by physiologproaches.
The logic approach is the most awkwardexcepthe others that have been tried.
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Logic in AI
Features of the logic approach to AIstarting in
Represent information by sentences in a logi
guage, e.g. first order logic, second order logic
logic.
Auxiliary information in tables, programs, stat
is described by logical sentences.
Inference is logical inferencededuction supple
by calculation and some form of nonmonotonic1980.
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Action takes place when the system infers
should do the action.
Observation of the environment results in se
in memory.
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Topics, methods and problems of logical A
deduction, nonmonotonic reasoning, theoriestion, problem solving,
The frame, qualification, and ramification pr
have been partly solved.
concepts as objects, contexts as objects, appro
objects
Elaboration tolerance (educate without brain s
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THE COMMON SENSE INFORMATIC SITUAT
Common sense: A structure composed of abilit
knowledge. Programs with common sense19
The common sense informatic situation, which
from a bounded informatic situation, has been diffi
define precisely.
Bounded informatic situations, e.g. chess po
take specific facts into account. In common senseis no limitation on what objects and facts may b
relevant.
New facts may require revising conclusions, pla
algorithms. Formal nonmonotonic reasoning, e.
cumscription and default logic, are important to
representing common sense reasoning in logic.
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Actions and other events often have only partly
able effects. Often not even probabillistic mod
available.
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COMMON SENSE INFORMATIC SITUATION
Specific theories, e.g. scientific theories, are em
in common sense. Skills are also embedded in csense.
Common sense physics: When two objects collid
is a noise. An object pushed off a table will fall
floor.
Common sense psychology: A person comes to
someone whom he thinks killed his fellow country The facts behind many human abilities are no
narily expressed in language but are often expres
language or logic.
Common sense abilities: Grasp object being to
Recognize a surface of an objectthe knife. Fum
plastic surface.
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Common sense facts: In(Rpocket, Knif e, N ow)
(x)(P lastic-feel(x) Surface(x, Knif e)),
Human-level common sense requires represent
up-to-now mental state as an object and reasoningit.
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EMBEDDING A SCIENTIFIC FACT IN SITUAT
CALCULUS
Scientific theories are embedded in common senthe formulas are embedded in natural language.
Galileos formula d = 12gt2 can be embedded in a
common sense theory of situation calculus by
Falling(b, s) V elocity(b, s) = 0 Height(b, s) = h
d = 1
2
gt2
(s)(time(s) = time(s) + t Height(b, s) = He
For controlling a robot (1) must be used in con
with facts about concurrent events.
The situation calculus formula connects Galile
mula to quantities that are defined in (mostly unob
situations to which the theory applies.
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Like other scientific formulas, Galileos formula
more in constructing theories than in planning ac
a specific situation. Robots may use (1), i.e. d
expanded into situation calcculus, directly if they mediately measure the physical quantities involved
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EMBEDDING A SKILL IN COMMON SENSE
philosophical path
Objects exist independent of perception.
Machine learning research is mistaken in concen
on classifying perception. Herbert Simons Bacon
for scientific discovery is limited by its concentra
discovering relations among observables.
A 3-d object is not a construct from 2-d view
learn about objects from view and by other mean
blind live in the same world as the sighted.
Draw an object you can only feel but cant
program that can get an object from a pocket is Drosophila.
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EMBEDDING A SKILL IN SITUATION CALCU
The skill of finding an object in a pocket can be
embedded in situation calculus.
In(Knif e, RP ocket, s) Holding(Knife,Result(Move(RHand, Interior(RP ocket)); FumbleFGrasp(Knife); Remove(RHand, RP ocket), s))
Alternatively,
In(Knif e, RP ocket, s) (finger surface)(surface Surfaces(Knife) finger Fingers(R((s)(T ouches(f inger, surf ace, s) Observes(Tousurface), s)))(Result(Move(RHand, Interior(RPocket);FumbleFor(Knife)), s)).
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Complications: Conscious guiding of the fumbling
ble until object is found, very little detailed infor
is needed, and very little is available. For examp
doesnt need information about the other objectpocket.
Query: What do we know about the physics of p
and how is it represented in the human brain, a
should robots represent it?
Keep trying a, and you will shortly achieve a situ
such that Holds(f, s). How should this be repr
logically?
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SPECIFIC ABILITIES IMBEDDED IN COMM
SENSE
Skills like walking, playing tennis
Scientific theories
AI programs, e.g. Mycin
A chess player and a chess program
Make a decision based determining which of t
tions leads to a better resulting situation. H
only maybe.
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INTERACTION OF SKILLS AND KNOWLED
Partial knowledge of the skills and of situation
Picking my knife from my pocket containing co
keys.
Interaction of observation with reasoning abouin logical AI, e.g. Filman, Reiter, Levesque,
han, Sandewall, Doherty.
It may be new to emphasize partial knowledg
effects of exercising a skill.
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CURRENT PROJECTDOMAIN DEPENDE
CONTROL FOR LOGICAL PROBLEM SOLV
General logical problem solvers without domaindent control experience combinatorial explosion.
There is a profusion of cut-down logics.
STRIPS should be a strategy for a logical p
solver. Likewise DASL.
Minsky proposed in 1956 that a geometry t
prover should only try to prove sentences true in
agram. Herbert Gelernter implemented it, but i
IBM decided IBM should not be seen as making a
but data-processing machines.
Selene Makarios works on domain dependent c
She has some results in reducing search in theworld.
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EXAMPLES OF CONTROL
When looking for feasible actions, dont subsitu
formulas of the form Result(a, s). This is part of S When trying to prove two triangles congruent a
have side a in one triangle equal to side a in the
try to prove the corresponding adjacent angles eq
In blocks world and heuristically similar problem
for moves to final position.
Josefina Sierra-Ibanez and more recently Selene
ios.
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APPEARANCE AND REALITY
The world is made of three-dimensional object
are only partly observable. History is only partly observable or even knowab
Reality is more persistent than appearance.
Appearance of a scene after an event depends
reality of the scene and not just on what could
served.
Pattern recognition and scientific discovery resea
not properly taken these facts into account.
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ELABORATION TOLERANCE
A collection of facts, e.g. a logical theory, is elab
tolerant to the extent that it can be readily elabwww.formal.stanford.edu/jmc/elaboration.html c
theory and extensive examples.
English language statements are very elaboratio
ant provided human common sense is available. sentences will almost always work.
Neural nets, connectionist systems, and presen
programs have almost no elaboration tolerance. Ex
T. Sejnowskis Nettalk cannot be elaborated to
Pinyin pronunciations of the letters x and q.
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Many elaborations of well constructed nonmo
logical theories can be accomplished just by addi
tences.
Example from Missionaries and Cannibals. T
an oar on each bank of the river, . . . .
Formalizing Elaboration Tolerance by Aarati Pa
a forthcoming Stanford dissertation.
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FREE WILL IN A DETERMINIST WORLD
We can make a situation calculus theory of a
more determinist by adding axioms asserting that events occur.
Human free will may consist of using a non-dete
theory to decide deterministically on an action.
Heres a minimal example of using a non-determin
ory within a determinist rule.
Occurs(Does(Joe,if Better-for(J oe, Result(Does(Joe,a1), s)
Result(Does(Joe, a2), s))then a1else a2
), s).
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Here Better-for-Joe(s1, s2) is to be understoodserting that Joe thinks s1 is better for him than s
If we take the actor as understood, as is commonuation calculus stdies, we get a shorter formula
Occurs(if Better(Result(a1, s), Result(a2), s)then a1else a2
), s).
Do animals, even apes, make decisions based oparing anticipated consequences? If not, can atrained to do it? Chess programs do. AccordDan Dennett, some recent experiments suggest thsometimes consider the consequences of altern
tions. Jane Goodall (personal communication) me that chimpanzees do.
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OTHER ASPECTS OF LOGICAL AI
non-monotonic reasoning
concepts as objects
contexts as objects
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REFERENCES
Murray Shanahan,The frame problem in artifici
ligence M.I.T. Press, 1997
What is artificial intelligenceJohn McCarthy h
formal.stanford.edu/jmc/whatisai.html.
My AI articles are all on www-formal.stanford.edu
These references are completely inadequate.
This research was supported by DARPA.