Common Sli

7/28/2019 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.

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