In Intr trod oduc uctio tion n to to Art rtifici ficial al - - PowerPoint PPT Presentation

CPSC 322, Lecture 1 Slide 1

In Intr trod

• duc

uctio tion n to to Art rtifici ficial al In Inte tellige genc nce e (A (AI) I)

Computer ter Sc Science ce cpsc3 c322 22, , Lectur ture e 1

May, y, 8, 2012

CPSC 322, Lecture 1 Slide 2

Lecture cture Ov Overview view

• Course Essentials
• What is AI?
• Representation and Reasoning
• Course Overview
• AI applications……

CPSC 322, Lecture 1 Slide 3

Pe People

• ple

Te Teachi hing ng As Assist stan ants ts

• Mahsa Imani

mimani@cs.ubc.ca

• Shafi

fiq Joty rjoty@cs.ubc.ca

• Na

Nathan an Tomer ntomer@cs.ubc.ca

Instr struc uctor tor

• Giuse

sepp ppe e Ca Carenini ( carenini@cs.ubc.ca; office CICSR 129)

CPSC 322, Lecture 1 Slide 4

Course urse Essential entials(1) s(1)

• Course

se web-pag ages es: www.cs.ubc.ca/~carenini/TEACHING/CPSC322-12/index.html

WebSearch: Giuseppe Carenini

• This is where most information about the course will be

posted, most handouts (e.g., slides) will be distributed, etc.

• CHECK IT OFTEN!
• Lectur

tures es:

• Cover basic notions and concepts known to be hard
• I will try to post the slides in advance (by 8AM).
• After class, I will post the same slides inked with the notes I

have added in class.

• Each lecture will include a set of learning goals:

Student can….

CPSC 322, Lecture 1 Slide 5

Course urse Essential entials(2) s(2)

• Te

Textbo tbook

• k: Artificial Intelligence, 2nd Edition, by Poole,

Mackworth.

• It’s free!
• It’s available electronically

http://www.cs.ubc.ca/~poole/aibook/

• We will cover at least Chapters: 1, 3, 4, 5, 6, 8, 9

CPSC 322, Lecture 1 Slide 6

Course urse Es Esse sentials(3) ntials(3)

• WebCT:

T: discussion board

• Use the discussion board for questions about

assignments, material covered in lecture, etc. That way

• thers can learn from your questions and comments!
• Use email for private questions (e.g., grade inquiries or

health problems).

• AI

AIspac ace e : online tools for learning Artificial Intelligence http://aispace.org/

• Under development

here at UBC!

CPSC 322, Lecture 1 Slide 7

Course urse Elements ments

• Pr

Practic tice e Ex Exercis ises es: : 0% (we may do some in class)

• As

Assign gnmen ents ts: 20%

• Midterm:

erm: 30%

• Fi

Final: l: 50%

If f your final grade is >= 20% higher than your midterm erm grade:

• Assignments: 20%
• Midterm: 15%
• Final: 65%

CPSC 322, Lecture 1 Slide 8

As Assi signments nments

• Th

There e will be four assign gnmen ents ts in total

• They will not necessarily be weighted equally
• Group

p work

• code questions:

you can work with a partner always hand in your ur own piece ce of code (stating who your partner was)

• written questions:

you may discuss questions with other students you may not look at or copy each other's written work you'll be asked to sign an honour code saying you've followed these rules

CPSC 322, Lecture 1 Slide 9

As Assi signments: nments: Late te Days ys

• Hand in by 9am on due day (in class or electronically)
• Yo

You get three e late days 

• to allow you the flexibility to manage unexpected issues
• additional late days will not be granted except under

truly exceptional circumstances

• A d

A day is define ned as: all or part of a 24-hour block of time

beginning at 9 am on the day an assignment is due

• if you've used up all your late days, you lose 20%

per day

CPSC 322, Lecture 1 Slide 10

Missing ssing As Assi signments gnments / M / Midterm term / Fi / Final al

Hopeful fully ly late days will cover almost all the reasons you'll be late in submitting assignments.

• However, something more serious like an extended illness

may occur 

• Fo

For all such h cases es: : you'll need to provide a note from your

doctor, psychiatrist, academic advisor, etc.

• If you miss:
• an assignment

ment, , your score will be reweighted to exclude that

assignment

• the midterm

rm, , those grades will be shifted to the final. (Thus, your

total grade = 80% final, 20% assignments)

• the final,

, you'll have to write a make-up final as soon as possible.

CPSC 322, Lecture 1 Slide 11

How w to to Ge Get t Help? p?

• Use the course discussi

sion

• n board on WebCT for questions
• n course material (so keep reading from it)
• Go to office hours (newsgroup is NOT a good substitute

for this) –

• Sh

Shafi fiq: Mon2pm pm (le learn arning ing Ce Center r X1 X150)

• Giuse

sepp ppe: e: Tue 2pm (CI CICSR CSR #129)

• Na

Nathan: an: Wed 2pm (learning ing Ce Center r X150)

• Mahsa:

a: Thu 2pm (learn arning ing Ce Center r X150) Can schedule by appointment if you can document a conflict with the official office hours

CPSC 322, Lecture 1 Slide 12

Get etting ting Hel elp p fr from

• m Oth

ther er St Stud uden ents ts? ? From

• m th

the We e Web? b? (Pla lagi giarism) arism)

• It

t is OK OK to talk wi with your classmat mates es about assignments; ments; le learning ing from m each other r is is good

• Bu

But you must:

• Not copy from others (with or without the consent of the

authors)

• Write/present your work completely on your own (code

questions exception)

• If

f they use externa rnal source ce (e.g., g., Web) in the assignments. Report this. e.g., “bla bla bla…..” [wikipedia]

CPSC 322, Lecture 1 Slide 13

Get etting ting Hel elp p fr from

• m Oth

ther er So Sour urces es? ? (Pl Plag agia iarism) rism)

When you are in do doubt wh whether er the line is crossed sed:

• Talk to me or the TA’s
• See UB

UBC o C offi fici cial l regulati ation

• ns on what constitutes plagiarism

(pointer in course Web-page)

• Ignorance of the rules will not be a sufficient excuse for

breaking them Any unjustified cases will be severely ely dealt wi with by the Dean’s Off ffice ice (that’s the official procedure)

• My advice: better to skip an assignment than to have

“academic misconduct” recorded on your transcript and additional penalties as serious as expulsion from the university!

CPSC 322, Lecture 1 Slide 14

To To Su Summarize arize

• All the course logistics are described in the course

Webpage

www.cs.ubc.ca/~carenini/TEACHING/CPSC322-12/index.html

WebSearch: Giuseppe Carenini (And summarized in these slides)

• Make sure you carefully read and understand them!

CPSC 322, Lecture 1 Slide 15

Wh What t is s In Inte telligence? lligence?

CPSC 322, Lecture 1 Slide 16

What t is Arti tificial ficial In Inte telli lligence? gence?

Tw Two definitio itions ns that have been propose sed:

• Systems that think

nk and act act like humans

• Systems that think

nk and act act ration

• nal

ally ly

CPSC 322, Lecture 1 Slide 17

Th Thinking inking and Ac Acti ting ng Humanly anly

Model l the cogniti itive ve funct ction

• ns

s of human beings gs

• Humans are our only example of intelligence: we

should use that example! Pr Problem ems: s:

• But... humans often think/act in ways that we

don't consider intelligent (why?)

• And... detailed model of how people's minds
• perate not yet available

CPSC 322, Lecture 1 Slide 18

Th Thinki inking ng Rati tionally

• nally

Ration

• nal

ality ity: an abstract “ideal'' of intelligence, rather than ``whatever humans think/do'‘

• Ancient Greeks invented syllogisms: argument

structures that always yield correct conclusions given correct premises

• This led to logic, and probabili

ilist stic c reasoning ing which we'll discuss in this course

• But correct sound reasoning is not always enough

“to survive” “to be useful”…

CPSC 322, Lecture 1 Slide 19

Acti ting ng (&thi thinking) nking) Rati tionally

• nally

This course will emphasize a view of AI as building agents nts: artifacts that are able to think and act rationally in their environments Rationality is more cleanl nly y defined ed than human behavior, so it's a better design objective

(Eg: “intelligent” vacuum cleaner: maximize area cleaned, minimize noise and electricity consumption) Agents that can answer queries, plan actions and

solve complex problems And when you have a rational agent you can always tweak it to make it irrational!

CPSC 322, Lecture 1 Slide 20

Wh Why y do we need ed intel tellige ligent nt agents? ents?

CPSC 322, Lecture 1 Slide 21

Ag Agents ents act cting ing in an envi vironment ronment

Representation & Reasoning

CPSC 322, Lecture 1 Slide 22

Wh What t is an agent? nt?

It has the following characteristics:

• It is situated in some environ

ronment ent

• does not have to be the real world---can be an abstracted

electronic environment

• It can make observ

rvati ations

• ns (perhaps imperfectly)
• It is able to act

act (provide an answer, send an email)

• It has goals or preferen

rence ces s (possibly of its user)

• It may have prior knowled

edge ge or beliefs fs, and some way of updatin ting beliefs efs based on new experiences (to reason, to make inferences)

CPSC 322, Lecture 1 Slide 23

Possible sible Gr Group up acti tivity vity

Search on the Web for an example of an AI system and try to answer some of these questions

• What does the application actually do? Was it

evaluated? Is it a fielde ded d system tem?

• Why is it intelli

llige gent? nt? Is it learnin ing?

• What is its environ

ronment ent?

• What are its observa

vatio ions ns ?

• What are its action
• ns?

s?

• Does it model goals

s or prefere erenc nces es?

• What AI

AI techn hnol

• log
• gy

y does it use?

CPSC 322, Lecture 1 Slide 24

Lecture cture Ov Overview view

• Course Essentials
• What is AI?
• Rep

epresenta esentatio tion n an and d Rea eason

• nin

ing

• Course Overview
• AI applications……

CPSC 322, Lecture 1 Slide 25

Wh What t do we need ed to to represent resent ?

• Th

The enviro ronm nmen ent t /world d : What different configurations (states tes / possi sible le worlds) can the world be in, and how do we denote them? Chessboard, Info about a patient, Robot Location….

• How the world works

s (we

we wi will focus s on)

• Const

strai aints: nts: sum of current into a node = 0

• Causal

al: : what are the causes and the effects of brain disorders?

• Ac

Action

• ns preconditions and effects: when can I

press this button? What happens if I press it?

CPSC 322, Lecture 1 Slide 26

Corresponding rresponding Reasoning soning Ta Tasks s / / Pr Problems

• blems
• Const

strai aint nt Sa Satisfactio faction – Fi Find state te that satis isfie fies s set

• f constra

train ints

• ts. E.g., What is a feasible schedule for

final exams?

• An

Answerin ring Query – Is a given proposi sition

• n true/l

e/lik ikel ely y given en what is known? ? E.g., Does this patient suffers from viral hepatitis?

• Pl

Plannin ing g – Fi Find sequence nce of actio ions ns to reach a goal state te / maximize ze utility

• ity. E.g., Navigate through

and environment to reach a particular location

CPSC 322, Lecture 1 Slide 27

Represent presentation ation and Reason soning ing Sy Syst stem em

• A (repres

esentatio ntation) language ge in which the environment and how it works can be described

• Computational (reason
• nin

ing) proced edur ures es to compute a solution to a problem in that environment (an answer, a sequence of actions) Bu But the choice of an appropriate R&R system depends on a key property of the environment and of the agent’s knowledge

CPSC 322, Lecture 1 Slide 28

Deterministic terministic vs. . Sto tochastic hastic (Uncertain) certain) Domains mains

• Se

Sensin ing g Uncertai tainty nty: Can the agent fully

• bserve the current state of the world?
• Ef

Effec ect t Uncertai tainty nty: Does the agent know for sure what the effects of its actions are? Chess Poker Factory Floor Doctor Diagnosis Doctor Treatment

CPSC 322, Lecture 1 Slide 29

Determinist terministic ic vs vs. . St Stoch chastic astic Domains ains

Historically, AI has been divided into two camps: those who focus on representations based on logic ic and those who prefer probabi bility lity. A few years ago, CPSC 322 covered logic, while CPSC 422 introduced probability:

• now we introduce both representational families in

322, and 422 goes into more depth

• this should give you a better idea of what's

included in AI Note: Some of the most exciting current research in AI is actually building bridges between these camps.

CPSC 322, Lecture 1 Slide 30

Lecture cture Ov Overview view

• Course Essentials
• What is AI?
• Representation and Reasoning
• Cou
• urse

rse Overv erview iew

• AI applications……

CPSC 322, Lecture 1 Slide 31

Modules dules we'l 'll l cover er in th this course: se: R&Rsys sys

En Enviro ronm nmen ent Pr Problem em

Query Planning Deterministic Stochastic Constraint Satisfaction Search Arc Consistency Search Search Logics STRIPS Vars + Constraints Value Iteration

• Var. Elimination

Belief Nets Decision Nets Markov Processes

• Var. Elimination

Stati atic Sequenti ntial al Representation Reasoning Technique

CPSC 322, Lecture 1 Slide 32

Dim imensi ensions

• ns of
• f Rep

epres esen entation tational al Com

• mpl

plex exity ity

We'v 've already dy discu cuss ssed ed:

• Reasoning tasks (Static vs. Sequential )
• Deterministic versus stochastic domains

So Some other r importan tant t dimensi sion

• ns

s of complex exity: ity:

• Explicit state or propositions or relations
• Flat or hierarchical
• Knowledge given versus knowledge learned from

experience

• Goals versus complex preferences
• Single-agent vs. multi-agent

CPSC 322, Lecture 1 Slide 33

Ex Explici plicit t St State te or propos positions itions

How do we model the environment?

• You can enumerate the states

tes of the world.

• A state can be described in terms of featur

tures es

• Often it is more natural to describe states in terms of

assignments of values to features (variables).

• 30 binary features (also called propositions) can

represent 230= 1,073,741,824 states.

Mars Ex Explorer er Ex Example Weather Temperature LocX LocY

CPSC 322, Lecture 1 Slide 34

Ex Explici plicit t St State te or propos positions itions or relatio ations ns

• States can be described in terms of object

cts and relati ation

• nsh

ship ips.

• There is a proposition for each relationship on

each “possible” tuple of individuals.

• Textbook example: One binary relation and 10

individuals can represents 102=100 propositions and 2100 states! Univer ersi sity y Ex Example Registred(S,C) Students (S) = { } Courses (C) = { }

CPSC 322, Lecture 1 Slide 35

Fl Flat t or hierarchical rarchical

Is it useful to model the whole world at the same level of abstraction?

• You can model the world at one level of abstraction:

flat at

• You can model the world at multiple levels of

abstraction: hierarc rchi hica cal

• Example: Planning a trip from here to a resort in Cancun,

Mexico

CPSC 322, Lecture 1 Slide 36

Kn Know

• wle

ledg dge e gi given en vs. . kno nowle ledg dge e le lear arne ned d fr from

• m

ex expe perience ience

The agent is provided with a model of the world

• nce and far all
• The agent can learn how the world works based
• n experience
• in this case, the agent often still does start out with

some prior knowl wledge

CPSC 322, Lecture 1 Slide 37

Go Goals als ve versus sus (co complex) plex) preferences ferences

An agent may have prefer eren ence ces

• e.g., there is some preference/ut

erence/util ility ity functi ction that describes how happy the agent is in each state of the world; the agent's task is to reach a state which makes it as happy as possible

An agent may have a goal goal that it wants to achieve

• e.g., there is some state

te or set of states tes of the world that the agent wants to be in

• e.g., there is some proposition

ition or set of propositi sition

• ns that the

agent wants to make true What beverage to order?

• The sooner I get one the better
• Cappuccino better than Espresso

Preferences can be complex…

CPSC 322, Lecture 1 Slide 38

Si Single ngle-agent agent vs vs. . Multiagent tiagent domains ains

Does the environment include other agents? Everything we've said so far presumes that there is only

• ne agent in the environment.
• If there are other agents whose actions affect us, it

can be useful to explici icitly ly model their r goals and beliefs efs rather than considering them to be part of the environment

• Other Agents can be: cooper

erative ative, competit titiv ive, or a bit

• f both

CPSC 322, Lecture 1 Slide 39

Dim imensi ensions

• ns of
• f Rep

epres esen entationa tational l Com

• mpl

plex exity ity in in CPSC32 322

• Reasoning tasks (Constraint Satisfaction /

Logic&Probabilistic Inference / Planning)

• Deterministic versus stochastic domains

So Some other r importan tant t dimensi sion

• ns

s of complex exity: ity:

• Explicit state or features or relations
• Flat or hierarchical
• Knowledge given versus knowledge learned from

experience

• Goals vs. (complex) preferences
• Single-agent vs. multi-agent

CPSC 322, Lecture 1 Slide 40

Lecture cture Ov Overview view

• Course Essentials
• What is AI?
• Representation and Reasoning
• Course Overview
• AI applications……

CPSC 322, Lecture 1 Slide 41

(Ad Adve versarial) rsarial) Se Search ch: : Checkers ckers

Game playing was one of the first tasks undertaken in AI Ar Arthur ur Sa Samuel at IBM wrote programs to play checkers (1950s)

• initially, they played at a strong

amateur level

• however, they used some (simple)

machine learning techniques, and soon outperformed Samuel

Source: IBM Research

Chinook’s program was declared the Man- Machine World Champion in checkers in 1994! …and complete etely ly solve ved by a program in 2007!

CPSC 322, Lecture 1 Slide 42

(Ad Adve versarial) rsarial) Se Search ch: : Chess ss

In 1996 and 1997, Gary Kasparov, the world chess grandmaster played two tournaments against Deep Blue, a program written by researchers at IBM

Source: IBM Research

CPSC 322, Lecture 1 Slide 43

(Ad Adve versarial) rsarial) Se Search ch: : Chess ss

Deep Blue’s Results in the first tournament:

• won 1 game, lost 3 and tied 1

first time a reigning world champion lost to a computer

Source: CNN

CPSC 322, Lecture 1 Slide 44

(Ad Adve versarial) rsarial) Se Search ch: : Chess ss

Deep Blue’s Results in the second tournament:

• second tournament: won 3 games, lost 2, tied 1
• 30 CPUs + 480 chess processors
• Searched 126.000.000 nodes per sec
• Generated 30 billion positions per move reaching

depth 14 routinely

CPSC 322, Lecture 1 Slide 45

CSP SPs: s: Crossword ssword Pu Puzzl zzles es

Source: Michael Littman

CPSC 322, Lecture 1 Slide 46

CSP SPs: s: Radio io link k fr frequency uency ass ssignment ignment

Source: INRIA

Assigning frequencies to a set of radio links defined between pairs of sites in order to avoid d interfe rfere renc nces es. Constraints on frequency depend on positio tion of the links ks and on physi sica cal l enviro ronme ment nt . Sample Constraint network

47

Example le: : SLS for RNA s secon

• nda

dary ry structu cture re design

RNA strand made up of four bases: cytosine (C), guanine (G), adenine (A), and uracil (U) 2D/3D structure RNA strand folds into is important for its function Predicting structure for a strand is “easy”: O(n3) But what if we want a strand that folds into a certain structure?

• Local search over strands

 Search for one that folds into the right structure

• Evaluation function for a strand

 Run O(n3) prediction algorithm  Evaluate how different the result is from our target structure  Only defined implicitly, but can be evaluated by running the prediction algorithm

RNA strand

GUCCCAUAGGAUGUCCCAUAGGA

Secondary structure Easy Hard

Best algorithm to date: Local search algorithm RNA-SSD developed at UBC [Andronescu, Fejes, Hutter, Condon, and Hoos, Journal of Molecular Biology, 2004]

CPSC 322, Lecture 1

Constraint nstraint optimi timizatio zation n problems blems

Optimization under side constraints (similar to CSP) E.g. mixed integer programming (software: IBM CPLEX)

• Linear program: max cTx such that Ax ≤ b
• Mixed integer program: additional constraints, xi  Z (integers)
• NP-hard, widely used in operations research and in industry

Transportation/Logistics: Supply chain Production planning SNCF, United Airlines management and optimization: UPS, United States software: Airbus, Dell, Porsche, Postal Service, … Oracle, Thyssen Krupp, SAP,… Toyota, Nissan, ...

48 CPSC 322, Lecture 1

CPSC 322, Lecture 1 Slide 49

Pl Planning anning & Sc & Scheduling: eduling: Logistics istics

Dynamic Analysis and Replanning Tool (Cross & Walker)

• logistics planning and scheduling for military transport
• used in the 1991 Gulf War by the US
• problems had 50,000 entities (e.g., vehicles); different

starting points and destinations

Source: DARPA

Same techniques can be used for non-military applications: e.g., Emergency Evacuation

CSP SP/lo /logic: gic: fo formal al ve verification ification

50

Hardware verification Software verification

(e.g., IBM) (small to medium programs) Most progress in the last 10 years based on: Encodings into propositional satisfiability (SAT)

CPSC 322, Lecture 1

CPSC 322, Lecture 1 Slide 51

Logic: gic: Cyc ycSecure Secure

“scans s a computer ter netwo work rk to build a f formal mal represe sent ntati ation

• n of

the network, based on Cyc’s pre-existing ontology of networking, security, and computing concepts: This formal representation also allows users to interact directly with the model of the network, allowing testing of proposed changes.”

Excerpted from: Shepard et al., 2005

• Kn

Knowl wledge dge Repres esen entat tatio ion

• Se

Semantic tic Web !

CPSC 322, Lecture 1 Slide 52

Pl Planning: anning: Sp Spacecraft cecraft Control trol

NASA: Deep Space One spacecraft

• perated autonomously for two days in May, 1999:
• determined its precise position using stars and

asteriods

despite a malfunctioning ultraviolet detector

• planned the necessary course adjustment
• fired the ion propulsion system to make this adjustment

Source: NASA

For another space application see the Spike system for the Hubble telescope

Source: cs221 stanford

Slide 53 CPSC 322, Lecture 1

CPSC 322, Lecture 1 Slide 54

Reasoning asoning under der Unce certai rtainty: nty: Diagnosis gnosis

Source: Onisko et al., 99

Bayes Net: to diagnose liver diseases

CPSC 322, Lecture 1 Slide 55

Source: Mike Cora, UBC

Reasoning asoning Under der Unce certai rtainty nty

Texture classification using Support Vector Machines

• foliage, building, sky, water

Reasoning asoning Under der Unce certai rtainty nty

E.g. motion tracking: track a hand and estimate activity:

• drawing, erasing/shading, other

Source: Kevin Murphy, UBC

Slide 56 CPSC 322, Lecture 1

Com

• mpu

puter ter Vis isio ion n (no not t ju just t fo for rob

• bot
• ts!)

!)

Jing, , Baluja, Ro Rowl wley,

, Goo

• ogl

gle: e: Fi Find ndin ing g Can anon

• nic

ical al Im Imag ages es

Slide 58 CPSC 322, Lecture 1

Source: cs221 stanford

Compare mpare low-level level fe features tures

Slide 59 CPSC 322, Lecture 1

Source: cs221 stanford

In Induced duced Gr Graph ph

Slide 60 CPSC 322, Lecture 1

Source: cs221 stanford

AI I - Mach chine ine Learning rning @google

• gle
• Spam/Porn Detection
• Which ad to place given a query
• Train Speech to search on mobile
• Machine Translation
• …..

CPSC 322, Lecture 1 Slide 61

• Highly Parallelizable EM + Map Reduce (simple

code to write)

• Stochastic Gradient Descent

Watso son : analyzes natural language questions and content well enough and fast enough to compete and win against champion players at Jeopardy!

CPSC 322, Lecture 1 Slide 62

Source: IBM

“This Drug has been shown to relieve the symptoms

• f ADD with relatively few side effects."
• 1000s

0s of algorit rithms hms and KBs KBs, ,

• 3

3 secs secs

CPSC 322, Lecture 1 Slide 63

Pl Planning anning Under er Unce certainty rtainty

Source: Jesse Hoey UofT 2007

Learning and Using POMD

MDP

models of Patient-Caregiver Interactions During Activities

• f Daily Living

Goal: l: Help Older adults living with

cognitive disabilities (such as Alzheimer's) when they:

• forget

et the proper r sequence ce of tasks that need to be completed

• they lose track

k of the steps that they have already completed.

CPSC 322, Lecture 1 64

Military litary applications: lications: eth thica cal l iss ssues ues

• Robot soldiers
• Existing: robot dog carrying

heavy materials for soldiers in the field

• The technology is there
• Unmanned airplanes
• Missile tracking
• Surveillance
• …

CPSC 322, Lecture 1 Slide 65

Pl Planning anning Under er Unce certainty rtainty

Helicopter control: MDP, reinforcement learning St States: es: all possible positions, orientations, velocities and angular velocities Final solution involves Deterministic search ch!

Source: Andrew Ng 2004

Dec ecision ision Th Theo eory: : Dec ecis ision ion Su Supp ppor

• rt

t Sy Systems stems

E.g., Computational Sustainability New interdisciplinary field, AI is a key component

• Models and methods for decision making concerning the management

and allocation of resources

• to solve most challenging problems related to sustainability

Often constraint optimization problems. E.g.

• Energy: when are where to produce green energy most economically?
• Which parcels of land to purchase to protect endangered species?
• Urban planning: how to use budget for best development in 30 years?

66

Source: http://www.computational-sustainability.org/

CPSC 322, Lecture 1

CPSC 322, Lecture 1 Slide 67

Multiagent ltiagent Sy Syst stems: ems: Po Poke ker

“In full 10-player games Poki is better than a typical low-limit casino player and wins consistently; however, not as good as most experts New programs being developed for the 2-player game are quite a bit better, and we believe they will very soon surpass all human players”

Source: The University of Alberta GAMES Group

Sear arch ch Space ce: 1.2 quintillion nodes

CPSC 322, Lecture 1 Slide 68

Multiagent ltiagent Sy Syst stems: ems: Robot

• t So

Soccer ccer

Source: RoboCup web site

Extre remely ely complex mplex

• Stochastic
• Sequence of actions
• Multiagent

robotic soccer competition was proposed by LCI (UBC) in 1992 (which became Robocup in 1997).

St Statistical tistical Mach chine ine Tr Translat slation ion

SEHR GEEHRTER GAST! KUNST, KULTUR UND KOMFORT IM HERZEN BERLIN. DEAR GUESTS, ART, CULTURE AND LUXURY IN THE HEART OF BERLIN. DIE ÖRTLICHE NETZSPANNUNG BETRÄGT 220/240 VOLT BEI 50 HERTZ. THE LOCAL VOLTAGE IS 220/240 VOLTS 50 HZ. DE EN

Source: cs221 Stanford

Slide 70 CPSC 322, Lecture 1

信 letter trust letters believe signal a letter believe that letter of confidence 说 自己 themselves said that say they said he say that said they themselves saying that he would say that said that she had saying that he has 仍 然 是 continues to be are still the main would still be continued to be remains one of remains one continues to be the still is remains an area still viewed by are always one of 是 总理 Prime Minister the Prime Minister is the Prime Minister 他 he He

• ther

his him

• ther

that he he was him to he is he has

• f his

他 信 Thaksin Thaksin Chinnawat and Joint Communique Dr Thaksin Joint Communique , Mr Thaksin in his letter his letter

• thers

他 信 也 Thaksin also 总理 , 拒绝 …… 辞 职 . resign . leaving their service .

• f leaving their service .

resigned as counsel .

他 信 也 说 自己 仍 然 是 总理 , 拒 绝 辞 职 .

Source: cs221 stanford

Slide 71 CPSC 322, Lecture 1

Zi Zite: te: a a personalize sonalized d magazine azine

… that gets smarter as you use it

CPSC 322, Lecture 1 Slide 72

CPSC 322, Lecture 1 Slide 73

CPSC 322, Lecture 1 Slide 74

Read ad Chp 1 TO TODO O fo for Th Thurs

• Read carefully Section 1.6 : “Example Applications”
• The Tutoring System
• The trading agent
• The autonomous delivery robot
• The diagnostic assistant
• If your student Number is:
• 13950076 62462080 26750125 32404105
• Come and talk to me

CPSC 322, Lecture 1 Slide 75

Examples mples

Which of these things is an agent agent, and why or why not?

• A soccer-playing robot?
• A rock?
• Machine Translator?
• A thermostat?
• A dog?
• A car?

Which of these things is an intell llig igen ent t agent, and why or why not?

CPSC 322, Lecture 1 Slide 76

Acti ting ng (&thi thinking) nking) Rati tionally

• nally

This course will emphasize a view of AI as building agents nts: artifacts that are able to think and act rationally in their environments

• they act appropriately given goals and circumstances
• they are fle

lexib ible le to changing environments and goals

• they learn from experience
• they make appropriate choices given perceptual and

computational limitations (sometimes they act without thinking!)

• They gather

r in informati rmation

• n (if cost less than expected gain)

CPSC 322, Lecture 1 Slide 77

Ac Actin ting Humanly anly

The original test involved typing back and forth; the `To Total l Tu Turing Te Test includes a video signal to test perception too

• But... is acting just like a person what we really want?
• For example, again, don't people often do things that we

don't don't consider intelligent?

The Tu Turing g Te Test

• Don't try to come up with a list of characteristics that

computers must satisfy to be considered intelligent

• Instead, use an operational definition: consider it in

intell llig igen ent t wh when people can't t tell a co computer uter apart from m other r people