Re Repr pres esent entati ationa onal l Di Dime mens nsio - - PowerPoint PPT Presentation

▶
re repr pres esent entati ationa onal l di dime mens nsio
SMART_READER_LITE
LIVE PREVIEW

Re Repr pres esent entati ationa onal l Di Dime mens nsio - - PowerPoint PPT Presentation

Jan 22, 2024 328 likes •586 views

Re Repr pres esent entati ationa onal l Di Dime mens nsio ions ns Computer Co ter Sc Science ce cpsc3 c322, 22, Lectur ture e 2 (Te Text xtbo book ok Chpt1) January, ary, 6, 2010 CPSC 322, Lecture 2 Slide 1 Depa

slide-1
SLIDE 1

CPSC 322, Lecture 2 Slide 1

Re Repr pres esent entati ationa

  • nal

l Di Dime mens nsio ions ns

Co Computer ter Sc Science ce cpsc3 c322, 22, Lectur ture e 2 (Te Text xtbo book

  • k Chpt1)

January, ary, 6, 2010

slide-2
SLIDE 2

Depa partme ment nt of Compu puter ter Science nce Undergr dergrad aduat uate e Events ts

Events ts this week How to Prepare for the Tech Career Fair Date: : Wed. Jan 6 Time: : 5 – 6:30 pm Location ion: : DMP 110 Resume Writing Workshop (for non- coop students nts) Date: : Thurs. . Jan 7 Time: : 12:30 – 2 p pm Location ion: : DMP 201 CSSS Movie Night Date: : Thurs.

  • s. Jan 7

Time: : 6 – 10 pm Location ion: : DMP 310 Movies: “Up” & “The Hangover” (Free e Popcorn & Pop) Drop-In In Resume Edition Session Date: : Mon. Jan 11 Time: : 11 am – 2 p pm Location ion: : Rm Rm 255, ICICS/C /CS S Bldg Industry stry Panel Speakers: rs: Managers rs from Google, , IBM, , Microso soft ft, , TELUS, , etc. Date: : Tues. Jan 12 Time: : Panel: 5:15 – 6:15 pm; Networki rking ng: : 6:15 – 7:15 pm Location ion: : Panel: DMP 110; Networki rking ng: : X-wing Undergrad Lounge Lounge Tech Career Fair Date: : Wed. Jan 13 Time: : 10 am – 4 p pm Location ion: : SUB Ballroom

slide-3
SLIDE 3

CPSC 322, Lecture 2 Slide 3

Lecture cture Ov Overview view

  • Rec

ecap ap fr from

  • m la

last t le lectu ture re

  • Representation and Reasoning
  • An Overview of This Course
  • Further Dimensions of Representational

Complexity

slide-4
SLIDE 4

CPSC 322, Lecture 2 Slide 4

Course urse Essential entials

  • Course

se web-pag age e : CHECK IT OFTEN!

  • Te

Textbo tbook

  • k: Available online and pdf on WebCT
  • We will cover at least Chapters: 1, 3, 4, 5, 6, 8, 9
  • WebCT:

T: used for textbook, discussion board….

  • AI

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

  • Lecture slides…
  • Midter

erm exam, Wed, Mar 10 (1.5 hours, regular room) Any conflict?

slide-5
SLIDE 5

CPSC 322, Lecture 2 Slide 5

Agents ents acti ting ng in an environ ironment ment

Representation & Reasoning

slide-6
SLIDE 6

CPSC 322, Lecture 2 Slide 6

Lecture cture Ov Overview view

  • Recap from last lecture
  • Rep

epresen esentati tation

  • n an

and Re d Reas ason

  • nin

ing

  • An Overview of This Course
  • Further Dimensions of Representational

Complexity

slide-7
SLIDE 7

CPSC 322, Lecture 2 Slide 7

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)

  • Co

Constr strain aints: ts: sum of current into a node = 0

  • Causal

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

  • Ac

Actions

  • ns preconditions and effects: when can I

press this button? What happens if I press it?

slide-8
SLIDE 8

CPSC 322, Lecture 2 Slide 8

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

Answe werin ring Qu Query – Is a given propositi sition

  • n true/l

e/like ikely ly 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

slide-9
SLIDE 9

CPSC 322, Lecture 2 Slide 9

Representation presentation and Reasoning soning System tem

  • 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

slide-10
SLIDE 10

CPSC 322, Lecture 2 Slide 10

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 knows for sure what the effects of its actions are? Chess Poker Factory Floor Doctor Diagnosis Doctor Treatment

slide-11
SLIDE 11

CPSC 322, Lecture 2 Slide 11

Deterministic terministic vs. . Sto tochastic hastic Domains ains

Historically, AI has been divided into two camps: those who prefer representations based on logic and those who prefer probability ility. 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 No Note: Some of the most exciting current research in AI is actually building bridges between these camps.

slide-12
SLIDE 12

CPSC 322, Lecture 2 Slide 12

Lecture cture Ov Overview view

  • Recap from last lecture
  • Representation and Reasoning
  • An

n Overv erview iew of

  • f Thi

his Cou

  • urse

se

  • Further Dimensions of Representational

Complexity

slide-13
SLIDE 13

CPSC 322, Lecture 2 Slide 13

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

slide-14
SLIDE 14

CPSC 322, Lecture 2 Slide 14

Lecture cture Ov Overview view

  • Recap from last lecture
  • Representation
  • An Overview of This Course
  • Fu

Furth ther er Dim imen ensions ions of

  • f Rep

epres esentatio entationa nal l Com

  • mpl

plexity exity

slide-15
SLIDE 15

CPSC 322, Lecture 2 Slide 15

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
slide-16
SLIDE 16

CPSC 322, Lecture 2 Slide 16

Expli plicit cit Sta tate te or propositions positions

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

slide-17
SLIDE 17

CPSC 322, Lecture 2 Slide 17

Expli plicit cit Sta tate te or propositions positions or relations ations

  • States can be described in terms of objects

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) = { }

slide-18
SLIDE 18

CPSC 322, Lecture 2 Slide 18

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

slide-19
SLIDE 19

CPSC 322, Lecture 2 Slide 19

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

slide-20
SLIDE 20

CPSC 322, Lecture 2 Slide 20

Go Goals als versus sus (complex) plex) prefer ferences ences

An agent may have prefer eren ence ces

  • e.g., there is some preferen

erence/ut ce/utility 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…

slide-21
SLIDE 21

CPSC 322, Lecture 2 Slide 21

Single ngle-agent agent vs. . Multi tiagen agent t 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 explic icitly itly 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
slide-22
SLIDE 22

CPSC 322, Lecture 2 Slide 22

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
slide-23
SLIDE 23

CPSC 322, Lecture 2 Slide 23

Next xt class ss

  • Assignment 0 due: submit

t electro troni nica cally lly and you you can't 't use late days

  • Hint:

t: AA AAAI AI is the main AI AI assoc

  • cia

iatio tion

  • Come to class ready to discuss the two example

les s

  • f fielded

ed AI AI agents ts you found

  • I'll show some pictures of cool applications in that

class

  • Read carefully Section 1.6 on textbook: “Example

Applications”

  • The Tutoring System
  • The trading agent
  • The autonomous delivery robot
  • The diagnostic assistant
slide-24
SLIDE 24

CPSC 322, Lecture 2 Slide 24