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CHAPTER 1:Introduction to Artificial Intelligence

By: Getaneh T.

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What is Intelligence?

• Intelligence:– “the capacity to learn and solve problems”

(Websters dictionary– in partic!lar"

• the ability to solve novel problems

• the ability to act rationally • the ability to act like humans

• #rti$icial Intelligence

– b!ild and !nderstand intelligent entities or agents– % main approaches: “engineering” vers!s

“cognitive modeling”

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What’s involved in Intelligence?

• #bility to interact &ith the real &orld– to perceive" !nderstand" and act– e.g." speech recognition and !nderstanding and synthesis– e.g." image !nderstanding– e.g." ability to ta'e actions" have an e$$ect

• easoning and )lanning

– modeling the e*ternal &orld" given inp!t– solving ne& problems" planning" and ma'ing decisions– ability to deal &ith !ne*pected problems" !ncertainties

• +earning and #daptation– &e are contin!o!sly learning and adapting– o!r internal models are al&ays being “!pdated”

• e.g." a baby learning to categori,e and recogni,e animals

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Academic isci!lines relevant to AI• )hilosophy +ogic" methods o$ reasoning" mind as physical

system" $o!ndations o$ learning" lang!age"rationality.

• -athematics ormal representation and proo$" algorithms"comp!tation" (!n decidability" (in tractability

• )robability/0tatistics modeling !ncertainty" learning $rom data

• 1conomics !tility" decision theory" rational economic agents

• 2e!roscience ne!rons as in$ormation processing !nits.

• )sychology/ ho& do people behave" perceive" process cognitive3ognitive 0cience in$ormation" represent 'no&ledge.

• 3omp!ter b!ilding $ast comp!tersengineering

• 3ontrol theory design systems that ma*imi,e an ob4ective$!nction over time

• +ing!istics 'no&ledge representation" grammars

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Histor" of AI

• 5678: early beginnings

– -c3!lloch 9 )itts: Boolean circ!it model o$ brain

• 56 ;: T!ring– T!ring: birth o$ #I– ?artmo!th meeting: =#rti$icial Intelligence“ name adopted

• 56 ;s: initial promise– 1arly #I programs" incl!ding– 0am!el

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Histor" of AI

• 56>>A 8: eality da&ns– eali,ation that many #I problems are intractable– +imitations o$ e*isting ne!ral net&or' methods identi$ied

• 2e!ral net&or' research almost disappears

• 56>6AC : #dding domain 'no&ledge– ?evelopment o$ 'no&ledge@based systems– 0!ccess o$ r!le@based e*pert systems"

• 1.g." ?12? #+• B!t &ere brittle and did not scale &ell in practice

• 56C>@@ ise o$ machine learning– 2e!ral net&or's ret!rn to pop!larity– -a4or advances in machine learning algorithms and applications

• 566;@@ ole o$ !ncertainty– Bayesian net&or's as a 'no&ledge representation $rame&or'

• 566 @@ #I as 0cience– Integration o$ learning" reasoning" 'no&ledge representation– #I methods !sed in vision" lang!age" data mining" etc

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HA#: from the movie $%%1

• 2001: A Space Odyssey

– classic science $iction movie $rom 56>6

• D#+– part o$ the story centers aro!nd an intelligent

comp!ter called D#+– D#+ is the “brains” o$ an intelligent spaceship– in the movie" D#+ can

• spea' easily &ith the cre&• see and !nderstand the emotions o$ the cre&• navigate the ship a!tomatically• diagnose on@board problems• ma'e li$e@and@death decisions• display emotions

• In 56>6 this &as science $iction: is it still science$ictionE

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Hal and AI

• HAL’s Legacy: 2001’s omputer as !ream and "eality – -IT )ress" 566 " ?avid 0tor' (ed.– disc!sses

• D#+ as an intelligent comp!ter• are the predictions $or D#+ reali,able &ith #I todayE

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Consider &hat might 'e involved in 'uildinga com!uter li(e Hal)*

• What are the components that might be !se$!lE– ast hard&areE– 3hess@playing at grandmaster levelE– 0peech interactionE

• speech synthesis• speech recognition• speech !nderstanding

– Image recognition and !nderstanding E– +earningE– )lanning and decision@ma'ingE

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Can &e 'uild hard&are as com!le+ as the 'rain?

• Do& complicated is o!r brainE– a ne!ron" or nerve cell" is the basic in$ormation processing !nit– estimated to be on the order o$ 5; 5% ne!rons in a h!man brain– many more synapses (5; 57 connecting these ne!rons

– cycle time: 5; @8seconds (5 millisecond

• Do& comple* can &e ma'e comp!tersE– 5; C or more transistors per 3)F– s!percomp!ter: h!ndreds o$ 3)Fs" 5; 5% bits o$ #-– cycle times: order o$ 5; @ 6seconds

• 3oncl!sion– 10: in the near $!t!re &e can have comp!ters &ith as many basic processing elements as o!r brain" b!t &ith

• $ar $e&er interconnections (&ires or synapses than the brain• m!ch $aster !pdates than the brain

– b!t b!ilding hard&are is very di$$erent $rom ma'ing a comp!ter behave li'e a brainH

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Can Com!uters 'eat Humans at Chess?

• 3hess )laying is a classic #I problem– &ell@de$ined problem– very comple*: di$$ic!lt $or h!mans to play &ell

• 3oncl!sion:– 10: today s comp!ters can beat even the best h!man

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1966 1971 1976 1981 1986 1991 1997

Ratings

Human World Champion Deep Blue

Deep Thought

P o

i n t s R a

t i n g s

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Can Com!uters Tal(?• This is 'no&n as “speech synthesis”

– translate te*t to phonetic $orm

• e.g." “$ictitio!s” @J $i'@tish@es– !se pron!nciation r!les to map phonemes to act!al so!nd

• e.g." “tish” @J seK!ence o$ basic a!dio so!nds

• ?i$$ic!lties– so!nds made by this “loo'!p” approach so!nd !nnat!ral– so!nds are not independent

• e.g." “act” and “action” • modern systems (e.g." at #T9T can handle this pretty &ell

– a harder problem is emphasis" emotion" etc• h!mans !nderstand &hat they are saying• machines don t: so they so!nd !nnat!ral

• 3oncl!sion:– 2L" $or complete sentences– 10" $or individ!al &ords

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Can Com!uters Recogni,e -!eech?

• 0peech ecognition:

– mapping so!nds $rom a microphone into a list o$ &ords– classic problem in #I" very di$$ic!lt

• “+ets tal' abo!t ho& to &rec' a nice beach”

• (I really said “MMMMMMMMMMMMMMMMMMMMMMMM”

• ecogni,ing single &ords $rom a small vocab!lary• systems can do this &ith high acc!racy (order o$ 66N• e.g." directory inK!iries

– limited vocab!lary (area codes" city names– comp!ter tries to recogni,e yo! $irst" i$ !ns!ccess$!l hands

yo! over to a h!man operator– saves millions o$ dollars a year $or the phone companies

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Recogni,ing human s!eech .ctd*/

• ecogni,ing normal speech is m!ch more di$$ic!lt

– speech is contin!o!s: &here are the bo!ndaries bet&een &ordsE• e.g." “Oohn s car has a $lat tire”

– large vocab!laries• can be many tho!sands o$ possible &ords• &e can !se conte+t to help $ig!re o!t &hat someone said

– e.g." hypothesi,e and test– try telling a &aiter in a resta!rant: “I &o!ld li'e some dream and s!gar in my co$$ee”

– bac'gro!nd noise" other spea'ers" accents" colds" etc– on normal speech" modern systems are only abo!t >;@ ;N

acc!rate

• 3oncl!sion:– 2L" normal speech is too comple* to acc!rately recogni,e– 10" $or restricted problems (small vocab!lary" single spea'er

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Can Com!uters 0nderstand s!eech?

• Fnderstanding is di$$erent to recognition:

– “Time $lies li'e an arro&” • ass!me the comp!ter can recogni,e all the &ords• ho& many di$$erent interpretations are thereE

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Can Com!uters 0nderstand s!eech?

• Fnderstanding is di$$erent to recognition:

– “Time $lies li'e an arro&” • ass!me the comp!ter can recogni,e all the &ords• ho& many di$$erent interpretations are thereE

– 5. time passes K!ic'ly li'e an arro&E– %. command: time the $lies the &ay an arro& times the

$lies– 8. command: only time those $lies &hich are li'e an arro&– 7. “time@$lies” are $ond o$ arro&s

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Can Com!uters 0nderstand s!eech?

• Fnderstanding is di$$erent to recognition:

– “Time $lies li'e an arro&” • ass!me the comp!ter can recogni,e all the &ords• ho& many di$$erent interpretations are thereE

– 5. time passes K!ic'ly li'e an arro&E– %. command: time the $lies the &ay an arro& times the

$lies– 8. command: only time those $lies &hich are li'e an arro&– 7. “time@$lies” are $ond o$ arro&s

• only 5. ma'es any sense"– b!t ho& co!ld a comp!ter $ig!re this o!tE– clearly h!mans !se a lot o$ implicit commonsense

'no&ledge in comm!nication

• 3oncl!sion: 2L" m!ch o$ &hat &e say is beyond thecapabilities o$ a comp!ter to !nderstand at present

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Can Com!uters #earn and Ada!t ?

• +earning and #daptation– consider a comp!ter learning to drive on the $ree&ay– &e co!ld teach it lots o$ r!les abo!t &hat to do– or &e co!ld let it drive and steer it bac' on co!rse &hen it heads

$or the emban'ment• systems li'e this are !nder development (e.g." ?aimler Ben,• e.g." #+)D at 3-F

– in mid 6; s it drove 6CN o$ the &ay $rom )ittsb!rgh to0an ?iego &itho!t any h!man assistance

– machine learning allo&s comp!ters to learn to do things.– many s!ccess$!l applications:

• reK!ires some “set@!p”: does not mean yo!r )3 can learn to$orecast the stoc' mar'et or become a brain s!rgeon

• 3oncl!sion: 10" comp!ters can learn and adapt" &henpresented &ith in$ormation in the appropriate &ay

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• ecognition v. Fnderstanding (li'e 0peech

– ecognition and Fnderstanding o$ Lb4ects in a scene• loo' aro!nd this room• yo! can e$$ortlessly recogni,e ob4ects• h!man brain can map %d vis!al image to 8d “map”

• Why is vis!al recognition a hard problemE

• 3oncl!sion:– mostly 2L: comp!ters can only “see” certain types o$ ob4ects !nder

limited circ!mstances– 10 $or certain constrained problems (e.g." $ace recognition

Can Com!uters see2?

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Can com!uters !lan and ma(e o!timal decisions?

• Intelligence– involves solving problems and ma'ing decisions and plans

– e.g." yo! &ant to ta'e a holiday in Bra,il• yo! need to decide on dates" $lights• yo! need to get to the airport" etc• involves a seK!ence o$ decisions" plans" and actions

• What ma'es planning hardE– the &orld is not predictable:

• yo!r $light is canceled or there s a bac'!p on the 7;– there are a potentially h!ge n!mber o$ details

• do yo! consider all $lightsE all datesE– no: commonsense constrains yo!r sol!tions

– #I systems are only s!ccess$!l in constrained planning problems

• 3oncl!sion: 2L" real@&orld planning and decision@ma'ing is still beyondthe capabilities o$ modern comp!ters

– e*ception: very &ell@de$ined" constrained problems

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-ummar" of -tate of AI -"stems in Practice

• 0peech synthesis" recognition and !nderstanding– very !se$!l $or limited vocab!lary applications– !nconstrained speech !nderstanding is still too hard

• 3omp!ter vision– &or's $or constrained problems (hand@&ritten ,ip@codes– !nderstanding real@&orld" nat!ral scenes is still too hard

• +earning– adaptive systems are !sed in many applications: have their limits

• )lanning and easoning– only &or's $or constrained problems: e.g." chess– real@&orld is too comple* $or general systems

• Lverall:– many components o$ intelligent systems are “doable” – there are many interesting research problems remaining

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Intelligent Agent

• ?e$inition: #n intelligent agent perceives itsenvironment via sensors and acts rationally!pon that environment &ith its actuators .

environmentagent

?

sensors

actuators

percepts

actions

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• 0ensors:– 1yes (vision " ears (hearing " s'in (to!ch " tong!e

(g!station " nose (ol$action " ne!rom!sc!lar system(proprioception

• )ercepts:– #t the lo&est level – electrical signals– #$ter preprocessing – ob4ects in the vis!al $ield

(location" te*t!res" colors" P " a!ditory streams(pitch" lo!dness" direction " P

• #ct!ators: limbs" digits" eyes" tong!e" P• #ctions: li$t a $inger" t!rn le$t" &al'" r!n"

carry an ob4ect" P

e*g*3 Humans

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4otion of an Artificial4otion of an ArtificialAgentAgent

environmentagent

?

sensors

actuators laser rangefinder

sonars

touch sensors

f f l

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4otion of an Artificial4otion of an ArtificialAgentAgent

environmentagent

?

sensors

actuators

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5acuum Cleaner World

Percepts: location and contents, e.g. [ , Dirt!"ctions: #eft, Right, $uc%, &o'p

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5acuum Agent 6unction

Percept Sequence Action[A, Clean] Right

[A, Dirty] Suck[B, Clean] Left[B, Dirty] Suck

[A, Clean], [A,Clean] Right

[A, Clean], [A, Dirty] Suck…

()

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Rational Agent

• What is rational depends on:– P er$ormance meas!re @ The per$ormance

meas!re that de$ines the criterion o$ s!ccess

– Environment @ The agents prior 'no&ledge o$ theenvironment– Act!ators @ The actions that the agent can

per$orm– - ensors @ The agent s percept seK!ence to date

• We ll call all this the Tas' 1nvironment()1#0

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5acuum Agent PEA-

• Performance 7easure : minimi,e energycons!mption" ma*imi,e dirt pic' !p.-a'ing this precise: one point $or eachclean sK!are over li$etime o$ 5;;; steps.

• Environment : t&o sK!ares" dirtdistrib!tion !n'no&n" ass!me actions aredeterministic and environment is static(clean sK!ares stay clean

• Actuators : +e$t" ight" 0!c'" 2oLp• -ensors : agent can perceive its location

and &hether location is dirty

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Automated ta+i drivings"stem

• Performance 7easure : -aintain sa$ety" reachdestination" ma*imi,e pro$its ($!el" tire &ear "obey la&s" provide passenger com$ort" P

• Environment : F.0. !rban streets" $ree&ays"tra$$ic" &eather" c!stomers" P

• Actuators : 0teer" accelerate" bra'e" horn"spea'/display" P

• -ensors : Qideo" sonar" speedometer"odometer" engine sensors" 'eyboard inp!t"microphone" G)0" P

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Pro!erties of Environments

• 6ull" 8'serva'le9Partiall" 8'serva'le – I$ an agent s sensors give it access to the complete state

o$ the environment needed to choose an action" theenvironment is full" o'serva'le .

– 0!ch environments are convenient" since the agent is$reed $rom the tas' o$ 'eeping trac' o$ the changes inthe environment.

• eterministic – #n environment is deterministic i$ the ne*t state o$ the

environment is completely determined by the c!rrentstate o$ the environment and the action o$ the agent.

– In a $!lly observable and deterministic environment" theagent need not deal &ith !ncertainty.

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Pro!erties of Environments

• -tatic9 "namic*– # static environment does not change &hile

the agent is thin'ing.

– The passage o$ time as an agent deliberates isirrelevant.– The agent doesn t need to observe the &orld

d!ring deliberation.• iscrete9Continuous*

– I$ the n!mber o$ distinct percepts and actionsis limited" the environment is discrete "other&ise it is continuous .

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-ome agent t"!es

• .%/ Ta'le driven agents – !se a percept seK!ence/action table in memory to $ind the ne*t action.

They are implemented by a (large loo(u! ta'le .• .1/ -im!le refle+ agents

– are based on condition action rules " implemented &ith an appropriateprod!ction system. They are stateless devices &hich do not have memoryo$ past &orld states.

• .$/ 7odel 'ased refle+ agents – have internal state " &hich is !sed to 'eep trac' o$ past states o$ the

&orld.• .;/

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.%/ Ta'le driven agents

• Ta'le loo(u! o$ percept@action pairs mapping $romevery possible perceived state to the optimal action$or that state

• Pro'lems

– Too big to generate and to store (3hess has abo!t5; 5%; states" $or e*ample– 2o 'no&ledge o$ non@percept!al parts o$ the

c!rrent state– 2ot adaptive to changes in the environmentR

reK!ires entire table to be !pdated i$ changes occ!r– +ooping: 3an t ma'e actions conditional on previo!sactions/states

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.1/ -im!le refle+ agents

• Rule 'ased reasoning to map $rom percepts tooptimal actionR each r!le handles a collection o$perceived states

• Pro'lems

– 0till !s!ally too big to generate and to store– 0till no 'no&ledge o$ non@percept!al parts o$state

– 0till not adaptive to changes in the environmentRreK!ires collection o$ r!les to be !pdated i$

changes occ!r– 0till can t ma'e actions conditional on previo!s

state

1/ i !l fl + g t

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.1/ -im!le refle+ agentarchitecture

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-im!le 5acuum Refle+ Agent

$!nction Qac!!m@#gent(Slocation"stat!s

ret!rns Action

i$ stat!s U !irty then ret!rn -uc(else i$ location U A then ret!rn Rightelse i$ location U # then ret!rn #eft

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.$/ 7odel 'ased refle+ agents

• 1ncode “internal state” o$ the &orld toremember the past as contained inearlier percepts.

• 2eeded beca!se sensors do not !s!ally

give the entire state o$ the &orld at eachinp!t" so perception o$ the environmentis capt!red over time. “0tate” is !sed toencode di$$erent =&orld states= thatgenerate the same immediate percept.

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.$/7odel 'ased agentarchitecture

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.;/

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E+am!le: Trac(ing aE+am!le: Trac(ing aTargetTarget

targetro*ot

+ The ro*ot must %eep the target in vie+ The target-s tra ector! is not %no n in advance+ The ro*ot ma! not %no all the o*stacles in advance+ /ast decision is re0uired

;/ Architecture for goal

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.;/ Architecture for goal'ased agent

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.=/ 0tilit" 'ased agents

• When there are m!ltiple possible alternatives" ho& todecide &hich one is bestE

• # goal speci$ies a cr!de distinction bet&een a happyand !nhappy state" b!t o$ten need a more generalper$ormance meas!re that describes “degree o$happiness.”

• Ftility $!nction 0: -tate Reals indicating ameas!re o$ s!ccess or happiness &hen at a givenstate.

• #llo&s decisions comparing choice bet&eencon$licting goals" and choice bet&een li'elihood o$s!ccess and importance o$ goal (i$ achievement is!ncertain .

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com!lete

utilit" 'ased agent

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-ummar": Agents

• #n agent perceives and acts in an environment" has anarchitect!re" and is implemented by an agent program.• Tas' environment – PEA- .P er$ormance 3 Environment"

Act!ators" - ensors /• #n ideal agent al&ays chooses the action &hich ma*imi,es its

e*pected per$ormance" given its percept seK!ence so $ar.• #n autonomous learning agent !ses its o&n e*perience ratherthan b!ilt@in 'no&ledge o$ the environment by the designer.

• #n agent !rogram maps $rom percept to action and !pdatesinternal state.

– Refle+ agents respond immediately to percepts.–

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Intelligent -"stems in >our Ever"da" #ife

• )ost L$$ice– a!tomatic address recognition and sorting o$ mail

• Ban's– a!tomatic chec' readers" signat!re veri$ication systems– a!tomated loan application classi$ication

• 3!stomer 0ervice– a!tomatic voice recognition

• The Web– Identi$ying yo!r age" gender" location" $rom yo!r Web s!r$ing– #!tomated $ra!d detection

• ?igital 3ameras– #!tomated $ace detection and $oc!sing

• 3omp!ter Games– Intelligent characters/agents

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AI A!!lications: 7achine Translation

• +ang!age problems in international b!siness– e.g." at a meeting o$ Oapanese" Vorean" Qietnamese and 0&edish investors"

no common lang!age

– or: yo! are shipping yo!r so$t&are man!als to 5% co!ntries– sol!tionR hire translators to translate– &o!ld be m!ch cheaper i$ a machine co!ld do this

• Do& hard is a!tomated translation– very di$$ic!ltH e.g." 1nglish to !ssian

– “The spirit is &illing b!t the $lesh is &ea'” (1nglish– “the vod'a is good b!t the meat is rotten” ( !ssian

– not only m!st the &ords be translated" b!t their meaning alsoH– is this problem “#I@complete”E

• 2onetheless....– commercial systems can do a lot o$ the &or' very &ell (e.g."restricted

vocab!laries in so$t&are doc!mentation– algorithms &hich combine dictionaries" grammar models" etc.– ecent progress !sing “blac'@bo*” machine learning techniK!es

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AI and We' -earch

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What’s involved in Intelligence? .again/

• )erceiving" recogni,ing" !nderstanding the real &orld

• easoning and planning abo!t the e*ternal &orld

• +earning and adaptation

• 0o &hat general principles sho!ld &e !se to achieve thesegoalsE

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ifferent T"!es of Artificial Intelligence

5. -odeling e*actly ho& h!mans act!ally thin'

%. -odeling e*actly ho& h!mans act!ally act

8. -odeling ho& ideal agents “sho!ld thin'”

7. -odeling ho& ideal agents “sho!ld act”

• -odern #I $oc!ses on the last de$inition– &e &ill also $oc!s on this “engineering” approach– s!ccess is 4!dged by ho& &ell the agent per$orms

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Acting humanl": Turing test

• T!ring (56 ; =3omp!ting machinery and intelligence“

• =3an machines thin'E= =3an machines behave intelligentlyE“

• Lperational test $or intelligent behavior: the Imitation Game

• 0!ggests ma4or components reK!ired $or #I:@ 'no&ledge representation

@ reasoning"@ lang!age/image !nderstanding"

@ learning

X!estion: is it important that an intelligent system act li'e a h!manE

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Thin(ing humanl"

• 3ognitive/ ational/ easoning 0cience approach

– Try to get “inside” o!r minds– 1.g." cond!ct e*periments &ith people to try to “reverse@engineer”

ho& &e reason" learning" remember" predict

• )roblems– -achines don t behave rationally

• e.g." ins!rance

– The reverse engineering is very hard to do

– The brain s hard&are is very di$$erent to a comp!ter program

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Thin(ing rationall"

• epresent $acts abo!t the &orld via logic

• Fse logical in$erence as a basis $or reasoning abo!t these $acts

• 3an be a very !se$!l approach to #I– 1.g." theorem@provers

• +imitations– ?oes not acco!nt $or an agent s !ncertainty abo!t the &orld

• 1.g." di$$ic!lt to co!ple to vision or speech systems

– Das no &ay to represent goals" costs" etc (important aspects o$real@&orld environments

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Acting rationall"

• ?ecision theory/1conomics

– 0et o$ $!t!re states o$ the &orld– 0et o$ possible actions an agent can ta'e– Ftility U gain to an agent $or each action/state pair

– #n agent acts rationally i$ it selects the action that ma*imi,es its “!tility”

• Lr e*pected !tility i$ there is !ncertainty

• 1mphasis is on a!tonomo!s agents that behave rationally(ma'e the best predictions" ta'e the best actions

– on average over time

– &ithin comp!tational limitations (“bo!nded rationality”

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-ummar" of this cha!ter

• #rti$icial Intelligence involves the st!dy o$:– a!tomated recognition and !nderstanding o$ signals

– reasoning" planning" and decision@ma'ing– learning and adaptation

• #I has made s!bstantial progress in– recognition and learning– some planning and reasoning problems

– Pb!t many open research problems

• #I #pplications– improvements in hard&are and algorithms UJ #I applications in ind!stry"

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