Human Level Sli

7/28/2019 Human Level Sli

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John McCarthy

http://www-formal.stanford.edu/jmc

2005 November 2

THE LOGICAL ROAD TO HUMAN LEV

Will we ever reach human level AIthe main am

AI research?

Sure. Understanding intelligence is a difficult sciebut lots of difficult scientific problems have been s

nothing humans can do that humans cant make

We, or our descendants, will have smart robot se

AI research should use AI Drosophilas, domains

informative about mechanisms of intelligence, not


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Who proposed human-level AI as goaloutside o

Alan Turing was probably firstin 1947, but all th

in AI took human level as the goal. AI as an indust

with limited goals came along in the 1970s. I do

of this research aimed at short term payoff is o

human-level AI. Indeed the researchers dont claim

Is there a Moores law for AI? Ray Kurzweil seperformance doubles every two years.

No.


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When will we get human-level AI?

Maybe 5 years. Maybe 500 years.

Will more of the same do it? The next factor of

puter speed. More axioms in CYC of the same

neural nets?

No.

Most AI research today is aimed at short term pay

conceptually difficult problems.


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Most likely we need fundamental new ideas. Mor

the ideas now being pursued by hundreds of resea

limited in scope by the remnants of behaviorist

philosophywhat Steven Pinker calls the blank you my ideas, but most likely they are not enough

My article Philosophical and scientific presupposit

AI, http://www.formal.stanford.edu/jmc/phil2.ht

explains what human-level AI needs in the way of


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REQUIREMENTS FOR HUMAN-LEVE

An ontology adequate for stating the effects

amples include situations, fluents, actions and oth

functions giving the new situations that result fro

can be told facts e.g. the LCDs in a laptop ar

glass. (stated absolutely but in an implicit contex

knowledge of the common sense worldfacts

3-d flexible objects, appearance including feel and

fects of actions and other events.extendable to


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the agent as one among many It knows abou

and their likes, goals, and fears. It knows how its a

with those of other agents.

independence A human-level agent must not be d

human to revise its concepts in face of experience,

or new information. It must be at least as capabl

reasoning about its own mental state and mental

elaboration tolerance The agent must be able

account new information without having to be re

person.


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relation between appearance and reality betwe

and their 2-d projections and also with the sensa

ing them. Relation between the course of events

observe and do.

self-awareness The agent must regard itself as

as an agent and must be able to observe its own

connects reactive and deliberated action e.g

removing ones keys from a pocket.

counterfactual reasoning If another car had com

when you passed, there would have been a head


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If the cop believes it, youll be charged with re

McCarthy and Costello on useful counterfactuals

reasons with ill-defined entitiesthe purposethe welfare of a chicken, the rocks of Mount Ev

that might have come over the hill.

These requirements are independent of whether th

based or an imitation of biology, e.g. a neural net


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APPROACHES TO AI

biologicalimitate human, e.g. neural nets, shou

tually, but theyll have to take a more general app

engineeringstudy problems the world presents, s

direct programming, genetic programming.

use logic and logical reasoning The logic approaawkwardexcept for all the others that have bee

the work with fmri makes it look like the logical

approaches may soon usefully interact.


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WHY THE LOGIC ROAD?

If the logic road reaches human-level AI, we will ha

understanding of how to represent the informatioable to achieve goals. A learning or evolutionary

achieve the human-level performance without the u

Leibniz, Boole and Frege all wanted to form

sense. This requires methods beyond what worke

mathematicsfirst of all formalizing nonmonoton

Since 1958: McCarthy, Green, Nilsson, Fikes, Re

Bacchus, Sandewall, Hayes, Lifschitz, Lin, Kow


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Perlis, Kraus, Costello, Parmar, Amir, Morgenste

Doherty, Ginsberg, McIlraith . . . and others I ha

Express facts about the world, including effects other events.

Reason about ill-defined entities, e.g. the welfa

Thus formulas like

Welfare(x, Result(Kill(x), s)) < W elfare(x, s) are s

even though Welfare(x, s) is often indeterminate.


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LOGIC

Describes how people thinkor how people think

The laws of deductive thought. (Boole, de M

Peirce). First order logic is complete and perhaps

Present mathematical logic doesnt cover all good

does cover all guaranteed correct reasoning.

More general correct reasoning must extend logic

monotonic reasoning and probably more. Some g

monotonic reasoning is not guaranteed to always p

conclusions.


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COMMON SENSE IN LOGICAL LANGUAGES

For every boy, theres a girl who loves only him

(b)(g)(Loves(g, b) (!b)Loves(g, b))This uses different sorts for boys and girls. There is

logical way of saying loves only him.

Block A is on Block B.

Variants: On(A, B), On(A , B , s), Holds(On(A, B), s

T op(B), V alue(Location(A), s) = V alue(T op(B), s).

Pat knows Mikes telephone number.

Knows(P at, T T elephone(MMike))


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THE COMMON SENSE INFORMATIC SIT

The common sense informatic situation is the key t

AI.

I have only partial information about myself and my

I dont even have a final set of concepts.

Objects of perception and thought are only partly often only approximately defined.

What I think I know is subject to change and elab


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There is no bound on what might be relevant. T

drosophila illustrates this common sense physics.

eter to find the height of a building.]

Sometimes we (or better it) can connect a bound

situation to an open informatic situation. Thus

blocks world can be used to control a robot stacki

A human-level reasoner must often do nonmonoto

Nevertheless, human reasoning is often very effec

Im in a world in which Im a product of evolution


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THE COMMON SENSE INFORMATIC SITUAT

The world in which common sense operates has

aspects.

1. Situations are snapshots of part of the world.

2. Events occur in time creating new situations. Aare events.

3. Agents have purposes they attempt to realize


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4. Processes are structures of events and situati

5. 3-dimensional space and objects occupy regioagents, e.g. people and physical robots are ob

can move, have mass, can come apart or com

larger objects.

6. Knowledge of the above can only be approxim

7. The csis includes mathematics, i.e. abstract

their correspondence with structures in the re


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8. Common sense can come to include facts disc

ence. Examples are conservation of mass and

of volume of a liquid.

9. Scientific information and theories are imbedd

sense information, and common sense is need

ence.


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BACKGROUND IDEAS

epistemology (what an agent can know aboutgeneral and in particular situations)

heuristics (how to use information to achieve

declarative and procedural information

situations


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SITUATION CALCULUS

Situation calculus is a formalism dating from 1964

ing the effects of actions and other events.

My current ideas are in Actions and other events in

culus - KR2002, available as www-formal.stanford.

They differ from those of Ray Reiters 2001 bo

however, been extended to the programming lang

Clear(x) Clear(l) At(x, l, Result(Move(xAt(y, l1) y = x At(y, l1, Result(Move(x,


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Going from frame axioms to explanation closure ax

oration tolerance. The new formalism is just as co

based on explanation closure but, like systems u

ioms, is additively elaboration tolerant.

The frame, qualification and ramification problem

and significantly solved in situation calculus.

There are extensions of situation calculus to conccontinuous events and actions, but the formalism

entirely satisfactory.


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CONCURRENCY AND PARALLELIS

In time. Drosophila = Junior in Europe and

york. When concurrent activities dont interactcalculus description of the joined activities nee

junction of the descriptions of the separate acthe joint theory is a conservative extension otheories. Temporal concurrency is partly done

In space. A situation is analyzed as compostions that are analyzed separately and then (iinteraction. Drosophilas are Go and the geLemmings game. Spatial parallelism is hardly


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INDIVIDUAL CONCEPTS AND PROPOS

In ordinary language concepts are objects. So be

CanSpeakW ith(p1, p2, Dials(p1, T elephone(p2), s))Knows(p1, T T elephone(pp2), s) Cank(p1, Dial(T

T elephone(Mike) = T elephone(Mary)TTelephone(MMike) = TTelephone(MMary)

Denot(MMike) = Mike Denot(MMary) = Mary(pp)(Denot(T elephone(pp)) = T elephone(Denot(ppKnows(P at, T T elephone(MMike))

Knows(P at, T T elephone(MMary))


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CONTEXTRelations among expressions evaluated in differen

C0 : V alue(T hisLecture, I) = JohnMcCarthy

C0 : Ist(U SLegalHistory, Occupation(Holmes) =C0 : Ist(U SLiteraryHistory, Occupation(HolmeC0 : F ather(V alue(U SLegalHistory, Holmes)) =V alue(U SLiteraryHistory, Holmes)

V alue(CAFdb,Price(GE610)) = V alue(CGEdb,Pri+V alue(CGEdb,Price(Spares(GE610)))

Can transcend outermost context, permitting intr

Here we use contexts as objects in a logical theory, an extension to logic. The approach hasnt beenbad.


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NONMONOTONIC REASONINGCIRCUMSP P (x . . . z)(P(x . . . z) P(x . . . z))P < P P P (P A)

Circm{E; C; P; Z} E(P, Z) (P

Z

)(E(P

, Z

)

In Circm{E; C; P; Z}, E is the axiom, C is a set oconstant, P is the predicate to be minimized, andpredicates that can be varied in minimizing P.

Ab(Aspect1(x)) flies(x)bird(x) Ab(Aspect1(x))bird(x) Ab(Aspect2(x)) flies(x)

penguin(x) Ab(Aspect2(x))penguin(x) Ab(Aspect3(x)) flies


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Let E be the conjunction of the above sentences.

Then Circum(E; {bird, penguin}; Ab; flies) implies

flies(x) bird(x) penguin(x), i.e. the things tha

birds that are not penguins.

frame, qualification and ramification problems

Conjecture: Simple abnormality theories arent en

(No matter what the language).

Inference to a bounded model


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SOME USES OF NONMONOTONIC REA1. As a communication convention. A bird may b

fly.

2. As a database convention. Flights not listed d

3. As a rule of conjecture. Only the known tools

4. As a representation of a policy. The meeting is

unless otherwise specified.

5. As a streamlined expression of probabilistic info

probabilities are near 0 or near 1. Ignore the risk o

by lightning.


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ELABORATION TOLERANCE

Drosophila = Missionaries and Cannibals: The sm

ary cannot be alone with the largest cannibal. Osionaries is Jesus Christ who can walk on water. T

that the river is too rough is 0.1.

Additive elaboration tolerance. Just add sentence

See www.formal.stanford.edu/jmc/elaboration.ht

Ambiguity tolerance

Drosophila = Law against conspiring to assault a f


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APPROXIMATE CONCEPTS AND THE

Reliable logical structures on quicksand semantic

Drosophila = {Mount Everest, welfare of a chicke

No truth value to many basic propositions.

Which rocks belong to the mountain?

Definite truth value to some compound propositio

concepts are squishy. Did Mallory and Irvine rea

Everest in 1924?


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HEURISTICS

Domain dependent heuristics for logical reasoning

Declarative expression of heuristics.

Wanted: General theory of special tricks

Goal: Programs that do no more search than hu

the 15 puzzle, Tom Costello and I got close. Sha

got closer.


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LEARNING AND DISCOVERY

Learning - what can be learned is limited by whatsented.Drosophila = chess

Creative solutions to problems.Drosophila = mutilated checkerboard

Declarative information about heuristics.

Domain dependent reasoning strategiesDrosophilas = {geometry, blocks world}

Strategy in 3-d world.Drosophila = Lemmings


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Learning classifications is a very limited kind of lea

Learn about reality from appearance, e.g 3-d re

appearance. See

www-formal.stanford.edu/jmc/appearance.html fo

zle.

Learn new concepts. Stephen Muggletons induc

gramming is a good start.


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ALL APPROACHES TO AI FACE SIMILAR P

Like humans AI systems must communicate in fac

grams or in objects. To communicate requires ve

edge of the mental state of the recipient.

Succeeding in the common sense informatic situ

elaboration tolerance.

It must infer reality from appearance.

Living with approximate concepts is essential


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Transcending outermost context, introspection.

Nonmonotonic reasoning


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INTUITIONS AND ARGUMENTS AGAINST

In 1975 Marvin Minsky argued that logic didnt h

tonic reasoning. Nonmonotonic extensions of logi

The connectionist argument of 1980: Logical AI h

human-level intelligence. Therefore, our way must

years have elapsed, and connectionism hasnt don

Your logical language cant express X. Hence

quate. Extend the language. Getting a univers

unsolvedrequires metamathematics in the langu


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People dont reason logically, e.g. Kahneman

examples. When people reason in opposition to

mistaken. Formal logic, starting with Aristotle

vented for communication among people and to imreasoning.

Present general first order logic programs do

on problems expressed in first order logic. Be

are neededincluding metamathematical reasonin

tirely on resolution was a mistake.

Godel showed incompleteness of first order arithm

ing showed undecideability of the halting problem.


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around these limitationswhich also apply to h

ing. As Turing (1930s), Gentzen (1930s) and Fe

showed, strengthening arithmetic is possible, but

complicated. Some very smart people, e.g. Penrosget it wrong, perhaps because of philosophical and


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QUESTIONS

What can humans do that humans cant make co

What is built into newborn babies that we havto build into computer programs? Semi-permaneobjects.

Is there a general theory of heuristics?

First order logic is universal. Is there a general figuage? Is set theory universal enough?

What must be built in before an AI system can leaand by questioning people?


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CAN WE MAKE A PLAN FOR HUMAN LE

Study relation between appearance and reality.

www-formal.stanford.edu/jmc/appearance.html

Extend sitcalc to full concurrency and continuo

Extend sitcalc to include strategies

Mental sitcalc

Reasoning within and about contexts, transcend


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Concepts as objectsas an elaboration of a t

concepts. Denot(TTelephone(MMike)) = T elephon

Uncertainty with and without numerical probabilit

of a proposition as an elaboration.

Heavy duty axiomatic set theory. ZF with abbre

defining sets. Programs will need to invent the E

the comprehension set former {x , . . . |E{x , . . .}}.

Reasoning program controllable by declaratively e

tics. Instead of domain dependent or reasoning st


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logics use general logic with set theory control

dependent advice to a general reasoning program

All this will be difficult and needs someone youngedgeable, and independent of the fashions in AI.

For the rest of us: Ask oneself: Where is my wo

to human-level AI?


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AI-HARD PROBLEMSadapted from Fanya

Used to describe problems or subproblems in AI, t

the solution presupposes a solution to the stron(that is, the synthesis of a human-level intelligenc

that is AI-hard is, in other words, just too hard.

Examples of AI-hard problems are The Vision Pr

ing a system that can see as well as a human) and

Language Problem (building a system that can uspeak a natural language as well as a human). T

pear to be modular, but all attempts so far (1996)

have foundered on the amount of context inform

telligence they seem to require.

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