lect9_artificialintelligence

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Fall 2007 CS 1:Introductions to Modern Computer Science

Artificial Intelligence (AI)

Professor Adnan Darwiche

[email protected]@cs.ucla.edu

http://cs.ucla.edu/~darwichehttp://cs.ucla.edu/~darwiche


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What is AI?What is AI?A field of computer science concerned with understanding andA field of computer science concerned with understanding and

building intelligent agents that can:building intelligent agents that can:

PerceivePerceive

UnderstandUnderstand

PredictPredict

ManipulateManipulate

LearnLearn

This is very hard, yet possible, quite useful and a lot of fun!This is very hard, yet possible, quite useful and a lot of fun!

Hardware versus software (internet) agentsHardware versus software (internet) agents

Monolithic approach unnecessary for usefulnessMonolithic approach unnecessary for usefulness


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What is AI?What is AI?BroadBroad goals of AI are shared with other disciplines,goals of AI are shared with other disciplines,

including philosophy, cognitive science (psychology) andincluding philosophy, cognitive science (psychology) and

neuroscience. Some key distinctions:neuroscience. Some key distinctions:

Understand versus buildUnderstand versus build

Rational versus humanRational versus human--likelike

SpecificSpecific goals of AI are shared with other disciplines,goals of AI are shared with other disciplines,

including linguistics (perception), statistics (learning),including linguistics (perception), statistics (learning),

and mathematical logic (understanding and prediction).and mathematical logic (understanding and prediction).


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What is AI?What is AI?AI is very old:AI is very old:

Philosophers have been thinking about reasoning, perceptionPhilosophers have been thinking about reasoning, perception

and other mental abil ities for over 2000 yearsand other mental abil ities for over 2000 years

AI is very recent:AI is very recent:

Arti ficial IntelligenceArti ficial Intelligence coined in 1956coined in 1956

Computers as tools for testing theories of intelligenceComputers as tools for testing theories of intelligence

AI is popular:AI is popular:

Largest number of applications to graduate program at UCLALargest number of applications to graduate program at UCLA

Often cited asOften cited as field I would most like to be infield I would most like to be in along withalong with

modern geneticsmodern genetics


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The Turing TestThe Turing Test Proposed by Alan Turing in 1950 as an operational test of intellProposed by Alan Turing in 1950 as an operational test of intell igence: fool aigence: fool a

human interrogator into believing the agent is a human. To passhuman interrogator into believing the agent is a human. To pass the test, onethe test, one

needs:needs:

Natural language processingNatural language processing

Knowledge representationKnowledge representation

Automated reasoningAutomated reasoning

Machine learningMachine learning

Avoided physical interaction between interrogator and agent (phyAvoided physical interaction between interrogator and agent (phy sical simulationsical simulation

is unnecessary for intelligence), excluding need for:is unnecessary for intelligence), excluding need for:

Computer vis ionComputer vision

RoboticsRobotics

See lecture by Prof.See lecture by Prof. Demetri TerzopoulosDemetri Terzopoulos


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The Intelligent Agent ArchitectureThe Intelligent Agent Architecture

EmbeddingEnvironment

Knowledge

Reasoning

LearningPerception:

written languagespeechvision

touchmouse click

Action:written language

speech

movementinformation display


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Knowledge RepresentationKnowledge Representation How do we represent knowledge?How do we represent knowledge?

Factual knowledge (facts)Factual knowledge (facts)

Uncertain knowledge (beliefs)Uncertain knowledge (beliefs)

What knowledge is relevant?What knowledge is relevant?

How do we acquire knowledge?How do we acquire knowledge?

From expertsFrom experts By conversion from other forms of knowledgeBy conversion from other forms of knowledge

By learning from experienceBy learning from experience


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KR: How do we represent knowledge?KR: How do we represent knowledge?FactsFacts BeliefsBeliefs

Knowledge:

There are four people: Roberta, Thelma, Steve,and Pete.Among them, they hold eight different jobs.

Each holds exactly two jobs.

The jobs are chef, guard, nurse, clerk, police officer(gender not implied), teacher, actor, and boxer.

The job of nurse is held by a male.The husband of the chef is the clerk.Roberta is not a boxer.Pete has no education past the ninth grade.

Roberta, the chef, and the police officer went

golfing together.

Question: Who holds which jobs?

Representations:

Propositional logic

First order logic

First order logic (Implicit versus explicit knowledge):

If HUSBAND(x,jobholder(chef)) then HASAJOB(x,clerk)FEMALE(jobholder(chef))

If HUSBAND(x,y), then MALE(x) and FEMALE(Y)

If HASAJOB(x,nurse) then GREATERTHAN(education(x),9)

Knowledge:

A few weeks after inseminating a cow, we have threepossible tests to confirm pregnancy. The first is ascanning test which has a false positive of 1% and a false

negative of 10%. The second is a blood test, which detects

progesterone with a false positive of 10% and a falsenegative of 30%. The third test is a urine test, which also

detects progesterone with a false positive of 10% and afalse negative of 20%. The probability of a detectableprogesterone level is 90% given pregnancy, and 1% givenno pregnancy. The probability that insemination will

impregnate a cow is 87%.

Question: Whats the belief in pregnancy given a postive

urine test but a negative blood test?

Representations:

Belief networks

Fuzzy logic


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ReasoningReasoning How can we formalize the reasoning process?How can we formalize the reasoning process?

Deduction: What is implied by a knowledge base?Deduction: What is implied by a knowledge base?

Belief revision: What beliefs to give up in case of a contradictBelief revision: What beliefs to give up in case of a contradict ion?ion?

Causality: What is the cause of an event?Causality: What is the cause of an event?

How can we reason efficiently?How can we reason efficiently?

TimeTime

SpaceSpace

FootprintFootprint


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Reasoning: Formalizing Belief RevisionReasoning: Formalizing Belief Revision

Oscar used to believe that he had given Victoria a gold ring at their wedding. He had bought their two wedding

rings at a jewelers shop in Casablanca. He thought it was a bargain. The merchant had claimed that the rings

were made of 24 carat gold. They certainly looked like gold, but to be on the safe side Oscar had taken the rings

to the jeweler next door who has testified to their gold content.

However, some time after the wedding, Oscar was repairing his boat and he noticed that the sulfuric acid he

was using stained his ring. He remembered from his school chemistry that the only acid that affected gold wasaqua regia. Somewhat surprised, he verified that Victorias ring was also sustained by the acid.

So Oscar had to revise his beliefs because they entailed an inconsistency. He could not deny that the rings

were stained. He toyed with the idea that, by accident, he had bought aqua regia rather than sulfuric acid, but

he soon gave up this idea. So, because he had greater confidencein what he was taught in chemistry than in

his own smartness, Oscar somewhat downheartedly accepted that the rings were not made of gold after all.

Consequently, he was convinced that both jewelers had been lying. He also came to believe that they were in

collusion with each other, although he was not completely certain of this.


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Reasoning: Formalizing CausalityReasoning: Formalizing Causality When do we say A caused B?When do we say A caused B?

Needed for explanationNeeded for explanation

Allow us to predict the futureAllow us to predict the future

Suggest ways to control future eventsSuggest ways to control future events

Moral responsibilityMoral responsibility

Legal liabili tyLegal liabili ty

It is not simply sufficiency, nor necessityIt is not simply sufficiency, nor necessity

Billy and Suzy both throw rocks at a bottle. Suzys arm is better than

Billys, so her rock gets to the bottle first and shatters it. Billys throw wasperfectly accurate, so his rock would have shattered the bottle had Suzys

missed. Did Billys throw cause the bottle to shatter? What if both rocks hit

the bottle simultaneously.


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Gene 1

Gene 2

Gene 3


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Applications of KR&RApplications of KR&RMedical diagnosisMedical diagnosis

Credit card fraudCredit card fraud

Theorem proving (Mathematics)Theorem proving (Mathematics)

Formal verificationFormal verification

Cognitive tasks: planning, explanation, etcCognitive tasks: planning, explanation, etc


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Embedding

EnvironmentKnowledge

Reasoning

LearningPerception:


touchmouse click


speech



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Natural Language UnderstandingNatural Language UnderstandingUnderstand natural language:Understand natural language:

List all employees that have been working here for more than twoList all employees that have been working here for more than two

years and have not gotten a raise since then.years and have not gotten a raise since then.

Generate natural language:Generate natural language:

There are actually only two such employees, do you just needThere are actually only two such employees, do you just need

their names?their names?

Text summarization:Text summarization:

HereHeres a 100 word abstract of the articles a 100 word abstract of the article

Machine translationMachine translation

Convert text from one language into another.Convert text from one language into another.


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Natural Language UnderstandingNatural Language UnderstandingWhy is it hard? Context and ambiguity.Why is it hard? Context and ambiguity.

Syntactic ambiguity:Syntactic ambiguity: They are cooking applesThey are cooking apples

Semantic ambiguity:Semantic ambiguity: She ran to the bankShe ran to the bank

Pragmatic ambiguity:Pragmatic ambiguity: Can you open the door?Can you open the door?

Two key approaches:Two key approaches: Classical: Provide the system with rich knowledge of the world,Classical: Provide the system with rich knowledge of the world,

perform thorough syntactic, semantic and pragmatic analysis,perform thorough syntactic, semantic and pragmatic analysis,

disambiguate using knowledge and reasoning.disambiguate using knowledge and reasoning. Modern: Rely on corpus and disambiguate using machineModern: Rely on corpus and disambiguate using machine

learning.learning.


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Ambiguity and ContextAmbiguity and ContextThey are cooking apples

sentence -> noun phrase , verb phrase

noun phrase -> noun

noun phrase -> determiner , noun

verb phrase -> verb , noun phrase


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Natural Language Understanding: Web TranslationNatural Language Understanding: Web Translation


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Natural Language Understanding: Eliza the Chatbot!Natural Language Understanding: Eliza the Chatbot!

Try it: http: //wwwTry it: http: //www--ai.ijs.si/elizaai.ijs.si/eliza--cgicgi --bin/eliza_scriptbin/eliza_script

Adnan: I want to give an introductory lecture on AI.Adnan: I want to give an introductory lecture on AI.

Eliza: Why do you want to give an introductoryEliza: Why do you want to give an introductory

lecture on AI?lecture on AI?

Adnan: Our department chair asked me to.Adnan: Our department chair asked me to.

Eliza: Can you elaborate on that?Eliza: Can you elaborate on that?

Adnan: He says multiple faculty members willAdnan: He says multiple faculty members willcontribute to this class.contribute to this class.

Eliza: Please go on.Eliza: Please go on.

Adnan: I don't think you understood what I just said.Adnan: I don't think you understood what I just said.

Eliza: Why do you not th ink I understood what youEliza: Why do you not th ink I understood what you

just said?just said?

Adnan: Because you seem to be rambling.Adnan: Because you seem to be rambling.

Eliza: Oh, i seem to be rambling.Eliza: Oh, i seem to be rambling.

Adnan: Absolutely.Adnan: Absolutely.

Eliza: Please go on.Eliza: Please go on.

Adnan: I give up!Adnan: I give up!

Eliza: Can you elaborate on that?Eliza: Can you elaborate on that?

Adnan: You just proved my claim.Adnan: You just proved my claim.

Eliza: Why do you say your claim?Eliza: Why do you say your claim?

Adnan: Because I made it.Adnan: Because I made it.

Eliza: Do any other reasons not come to mind?Eliza: Do any other reasons not come to mind?

Adnan: I think the class have gotten the pointAdnan: I think the class have gotten the point

about you by now.about you by now.

Eliza: Do you wish to think the class haveEliza: Do you wish to think the class have

gotten the point about me?gotten the point about me?

Adnan: Yes.Adnan: Yes.

Eliza: Are you sure?Eliza: Are you sure?

..


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Embedding

EnvironmentKnowledge

Reasoning

LearningPerception:


touchmouse click


speech



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Machine LearningMachine Learning

Using experiences and observations to improve future performanceUsing experiences and observations to improve future performance

(actions):(actions):

What aspect of performance to be improved?What aspect of performance to be improved?Irrelevant aspects of the world, how the world evolves, what areIrrelevant aspects of the world, how the world evolves, what are desirable/undesirabledesirable/undesirable

situations.situations.

What feedback is available?What feedback is available?

Supervised, Unsupervised, Reinforcement learning.Supervised, Unsupervised, Reinforcement learning.

How to represent the output of a learning process?How to represent the output of a learning process?

Logical knowledge, Probabilistic knowledge, Neural networks.Logical knowledge, Probabilis tic knowledge, Neural networks.

Supervised: Give observations and actions they should lead toSupervised: Give observations and actions they should lead to Unsupervised: Give observations only (find patterns )Unsupervised: Give observations only (find patterns )

Reinforcement: Give positive/negative feedback on actionsReinforcement: Give positive/negative feedback on actions


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Supervised Learning: Character RecognitionSupervised Learning: Character Recognition

Neural Networks


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Unsupervised Learning: Recommender SystemsUnsupervised Learning: Recommender Systems

EE--commercecommerce

Recommend products basedRecommend products based

on previous purchases oron previous purchases or

clickclick--stream behaviorstream behavior

Ex: Amazon.comEx: Amazon.com

Information sitesInformation sites

Rate items based onRate items based on

previous user ratingsprevious user ratings

Ex: MovieLens, JesterEx: MovieLens, Jester


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JohnJohn 55 -- 33 22

SamSam -- 44 11 55

CindyCindy 33 -- 55 --

BobBob 55 11 3.53.5 1.71.7

LearnedKnowledge

Offline Learning

Reasoning

Unsupervised Learning: Recommender SystemsUnsupervised Learning: Recommender Systems


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PlanningPlanning Finding a sequence of actions that will achieve a goalFinding a sequence of actions that will achieve a goal

Input:Input:

Actions (preconditions, effects)Actions (preconditions, effects)

Initial, Goal statesInitial, Goal states

Knowledge of world (physics )Knowledge of world (physics)

Output:Output: Plan:Plan:

Conditional (contingency)Conditional (contingency)

Total/PartialTotal/Partial

Sensorless (conformant)Sensorless (conformant)

Autonomous Sciencecraft Experiment:Autonomous Sciencecraft Experiment:

uses onuses on--board science analysis andboard science analysis and

rere--planning to radically increase scienceplanning to radically increase science

return by enabling intelligent downlinkreturn by enabling intelligent downlink

selection and autonomous retargeting.selection and autonomous retargeting.

MultiMulti--Rover Integrated Science UnderstandingRover Integrated Science Understanding

System:System:develops architectures and technologiesdevelops architectures and technologies

for command and control of multifor command and control of multi --rover groupsrover groups

for p lanetary exploration.for p lanetary exploration.


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NASA: Autonomous Sciencecraft ExperimentNASA: Autonomous Sciencecraft Experiment

Use onboard decisionUse onboard decision--making to detect, analyze, and respond tomaking to detect, analyze, and respond to

science events, and to downlink only the highest value science dscience events, and to downlink only the highest value science data.ata.

Radically increase science return by enabling intelligent downliRadically increase science return by enabling intelligent downlinknk

selection and autonomous retargeting.selection and autonomous retargeting.

AI Technology Used:AI Technology Used:

Image analysisImage analysis

Planning, schedulingPlanning, scheduling

and recoveryand recovery

Mission status updates:Mission status updates:http://wwwhttp://www --aig.jpl.nasa.gov/public/planningaig.jpl.nasa.gov/public/planning


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RoboticsRobotics

Robots: Physical agents that perform tasks byRobots: Physical agents that perform tasks by

manipulating the world. Equipped with:manipulating the world. Equipped with:

Effectors: legs, wheels, joints, grippersEffectors: legs, wheels, joints, grippers

Sensors: cameras, ultrasound, gyroscopes, accelerometers.Sensors: cameras, ultrasound, gyroscopes, accelerometers.

Common categories of robots:Common categories of robots: Manipulators (robot arms): factory assembly lines, internationalManipulators (robot arms): factory assembly lines, international

space station.space station.

Mobile robots: unmanned land/air/water vehicles, planetaryMobile robots: unmanned land/air/water vehicles, planetaryroversrovers

Mobile robots with manipulators: humanoid (mimic human torso)Mobile robots with manipulators: humanoid (mimic human torso)


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Robotics: Manipulators in manufacturingRobotics: Manipulators in manufacturing


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Robotics: HumanoidsRobotics: Humanoidshttp://asimo.honda.com/inside_asimo_movies.asp


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Robotics: DARPA Grand ChallengeRobotics: DARPA Grand Challenge

2004 Challenge:

142 miles from Barstow, California to Primm, Nevada

$1M prize

15 robots qualified, none finished the race

7.4 miles was furthest distance

2005 Challenge:

132 miles in the Mojave desert$2M prize

24 robots qualified, 5 finished the race

Top team: 132 miles in 6 hours, 53 minutes.


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Robotics: DARPA Grand ChallengeRobotics: DARPA Grand Challenge


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MS student project at CS/UCLA using LEGOs Mindstorm Kit

Robot connected to a laptop that performs reasoning.

The goal of the robot is to play Mastermind autonomously

with a human player, with the robot trying to break thesecret pattern. The robot must sense the game state,decide its actions, and control the game pieces in real time.


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Faculty at CS/UCLA doing AIFaculty at CS/UCLA doing AI--related researchrelated research

Adnan DarwicheAdnan Darwiche

Mike DyerMike DyerPetros FaloutsosPetros Faloutsos

Rich KorfRich Korf

Judea PearlJudea Pearl

Stefano SoattoStefano Soatto

Demetri TerzopoulosDemetri Terzopoulos

Song Chun ZhuSong Chun Zhu


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Thank you!Thank you!

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lect9_artificialintelligence