Teaching AI Undergraduate 10.Summary

8/7/2019 Teaching AI Undergraduate 10.Summary

1/102

SummarySummary

Introduction to Artificial Intelligence


2/102

AI:Summary 2

Understand and BUILDintelligent entities

Two Views: Weak AI (Turing Test); Strong AI (ChineseRoom Objection)

Six Approaches: Symbolic AI; Connectionism; MachineLearning; Nouvelle AI; Simulated Evolution; Swarm

Intelligence

Intelligent Systems: Expert Systems; LISP andPROGLOG; Optical, DNA, and Quantum Computing


3/102

SymbolicAI

SymbolicAI

Chapter 2-3


4/102

AI:Summary 4

Search and ProblemSearch and Problem--SolvingSolving

State Space

- five elements (S, F, C, I, G)

- the solution is corresponding with apath from one of initial

states to one of goal states

And/Or Graph

- embody theproblem reduction

- the goal is to determine whether the root node is solvable

- the solution is a sub-graph leading to the solvability of the

root node


5/102

AI:Summary 5

Effectiveness and Efficiency of search algorithms are

contradictions to one another, thoroughly considering their

completeness, optimality, and complexity. The corresponding

design strategies include:blind vs. heuristic, local vs. global,

and feasible vs. optimal.

Graph is gradually generated through expanding nodes in the

search process until a goal state is found out (state space) or

the initial node is labeled solvable or unsolvable (and/or graph).


6/102

AI:Summary 6

The key difference between different search strategies is in the

order of node expanding.

Blind search does not consider the particularity of the problem,

which includes Bread-first search, Depth-first search, Depth-

limited search and Iterative deepening search.

Heuristic search employ the heuristic informationprovided by

the problem to evaluate the costs of nodes. The node with

lowest cost will be selected to expand.

A* algorithm is a well-known heuristic search algorithm for

state space, which guarantee to find out the optimal solution

through throwing a constraint on the heuristic function.


7/102

AI:Summary 7

Admissible heuristicsAdmissible heuristics

A heuristic h(n) is admissible if for every node n,

h(n) h*(n), where h*(n) is the true cost toreach the goal state from n.

An admissible heuristic never overestimates thecost to reach the goal, i.e., it is optimistic

Example: hSLD(n) never overestimates the actual road

distance.

Theorem: Ifh(n) is admissible, A* is optimal

for tree search


8/102

AI:Summary 8

2.3

2.13 A*2.3

a, b, c, da, b, c, d10 (1,1,1,1) (0, 0, 0, 0)

{}{}(1,1,1,1)(0,1,0,1)(1,1,0,1)(0,0,0,1)(1,0,1,1)(0,0,1,0)(

1,0,1,0)(0,0,0,0)


9/102

AI:Summary 9

hh((xx)=)=

(1,1,1,1)h=3

f=3

(0,0,1,1)h=

f=(0,1,0,1)

h=2

f=3(0,1,1,0)

h=

f=

(1,1,0,1)h=2

f=4

(0,0,0,1)h=1

f=4(0,1,0,0)

h=1

f=4

(1,0,0,1)h=

f=

(1,0,1,1)h=2

f=6 (1,1,0,0)h=

f= (1,1,1,0)

h=2

f=6

1

0

2

3 4

5


10/102

AI:Summary 10

(1,0,1,1) h=2f=6

(0,0,1,0)h=1

f=6

(0,0,0,1)h=1

f=6

(1,0,1,0)h=1

f=8(1,1,1,0)

h=2

f=8

(0,0,0,0)h=0

f=7

hh((xx)=)=

6

(1,0,0,1)h=

f=

7

9

10

(1,1,1,0)h=2

f=68


11/102

AI:Summary 11

Game treeData structure for representing game process

= And/Or graph in structure when considering best achievable

payoff against best play

Minimax algorithm

- Cut offthe game tree for real decision-making

- Evaluate the game state

- choose move to position with highest minimax value!

- pruning

Improve search efficiency throughpruning some

unnecessary branches in the process ofdepth-limited search


12/102

AI:Summary 12

2.21

-


13/102

AI:Summary 13

Knowledge and ReasoningKnowledge and Reasoning

Reasoning is the process ofmaking decision basedon facts and knowledge.

- Mechanisms

deduction vs. induction; certainty vs. uncertainty,

monotonic vs. non-monotonic

- control strategies

inference direction, conflict resolution, search

Knowledge representation- Facts, Rules and Meta-knowledge

- Declarative vs. Procedural knowledge


14/102

AI:Summary 14

Two languages of knowledge representation

- Propositional logic and first-order logic

Sentences: Constants, Predicates, Functions, Variables,

Connectives, Quantifiers

- Production rule and system

condition-action rules

system structure: working memory, rule base and interpreter

working model: match-resolve-act cycle


15/102

AI:Summary 15

Syntax of FOL: Basic elementsSyntax of FOL: Basic elements

Constants E.g., John, 2, Sun,...

Functions E.g., Sqrt, FatherOf,...

Variables E.g., x, y, a, b,... Predicates E.g., Brother, >,...

Connectives , , , ,

Quantifiers ,

Term


16/102

AI:Summary 16

3.9

1

2

3

4

5

6

7

)()( xiansummerhotxiansummerdry

choudoufuxlikexpersonx ,p

shuihuxreadsanguoxlikereadxpersonx ,, p

xiongzhangIwantfishIwant ,,

xybiggeryintegerxrealyx ,p

ybioxselecthistoryxselectxstudentx log,, p

yxdanceyxworkyxdanceyxworkysaturdayxstudentyx ,,,,


17/102

AI:Summary 17

Production RuleProduction Rule

P Q

or IF P THEN Q (certainty factor )

AntecedentCondition

ConsequentConclusion or Action

E.g.,

IF An animal has teeth, claw, andeyes

staringinto the frontage

Then the animalis carnivore (0.6)


18/102

AI:Summary 18

ProductionProduction--rule Systemrule System

Control System

(InferenceEngine)

(Interpreter)

Rule base Global Database (Facts)

(working memory)

Structure

ProcedureRule Matching Conflict Resolution RuleExecution

StopConditions


19/102

AI:Summary 19

First-order inference rules are fundamentsin first-order inference

Validity and Satifiability

Equivalence and Entailment

Unification is necessary in first-orderinference

Most general unification


20/102

AI:Summary 20

3.18

1

2

3

4

5

),(),,( yxPbaP _ aybxa ,

),(),,( zyPbxfP _ azbyxf ,

),(),,( bfyPyxfP

),,(),,,( bfafxxyyfP

),(),,( xyPyxP _ ayx


21/102

AI:Summary 21

Resolution is a kind ofreduction toabsurdity, which prove facts=>conclusion by proving factsconclusionunsatisfiable, i.e. by deriving the emptyclause

conjunctive normal form

resolution strategies


22/102

AI:Summary 22

Resolution RuleResolution Rule

Eliminate complementary literals in twosentences, the disjunction of remaining parts oftwo sentences construct the resolvent clausewhich is the entailment of two sentences above.

Literals are co ple entary if one is the negation ofthe other.

Apply resolution steps to CNF(KB ); complete

for inference when empty clause is reached.


23/102

AI:Summary 23

3.14

3

1. Eliminate biconditionals and implications

2. Move

inwards

yxRyxQyxPyx ,,, p

yxRyxQyxPyx ,,,

3. Standardize variables

4. Skolemize: a more general form of existential instantiation

yxRyxQyxPyx ,,,

xfxxfxxfxPx ,,,

Table 3-5 (P87) + Table 3-2 (P83)


24/102

AI:Summary 24

5. Drop universal quantifiers:

6. Distribute over :

xfxxfxxfxP ,,,

xfxxfxxfxxfxP ,,,,

Table 3-5 (P87) + Table 3-2 (P83)

Clause 1 Clause 2

Skeleom


25/102

AI:Summary 25

3.14

1

2

4

5

),(),( yxQyxPyx yxQyxPyx ,, p

),(),,( vuQyxP

yxQyxP ,,

zxRyxQyxPzyx ,,, p

yxfxRyxQyxP ,,,,

wzxRwvuzyxQwvuzyxPwvuzyx ,,,,,,,,,,,,

vzgvzfzyxvzgvzfzyxP ddddddd ,,,,,,,,,,,,,


26/102

AI:Summary 26

3.19 A B C D

E

1

qPzP sPqP

lPsP

sPzP lPqP

2

3

zP


27/102

AI:Summary 27

4.


28/102

AI:Summary 28

3.20

1

2MaryBill

MaryTom

1

xWyxByx p ,

xWyxSyx p ,

BillMaryS ,


29/102

AI:Summary 29

2.

xWyxB , uWvuS ,

BillMaryS ,

3.

TomMaryB ,

4.


30/102

AI:Summary 30

xWyxB , TomMaryB ,

MaryW uWvuS ,

vMaryS , BillMaryS ,

NIL

Mary/x, Tom/y

Mary/u

Bill/v


31/102

Artificial Neural NetworksArtificial Neural Networks

Chapter 4


32/102

AI:Summary 32

Artificial Neural Networks are massivelyparallel adaptive networks ofartificialneurons which are intended to abstractand model some of the functionality of the

human nervous system.

Basic elements of artificial neurons A set ofconnecting links

A combination function An activation function


33/102

AI:Summary 33

Artificial NeuronsArtificial Neurons

M-P Neuron

x1

x2

xn

y

1

2

n

!!

!

n

i

iixffy

1

U[W

Input Output

Threshold

McClloch and Pitts, , 1943

f:Activation

Function


34/102

AI:Summary 34

Combination FunctionsCombination Functions

Weighted Sum

Radial Distance

n

i

iixg1

U[\ X

n

i

iixffy1

U[\

!!

!

n

i

iixffy0

[\

!

!!n

i

ii cx1

2C\

n

i

i cxffy1

2\


35/102

AI:Summary 35

Activation FunctionsActivation Functions

\

)(\f

\

)(\f

\

)(\f

\

)(\f

\

)(\f

\

)(\f

Threshold Linear Saturating Linear

Logistic SigmoidHyperbolic

tangent SigmoidGaussian


36/102

AI:Summary 36

Two key problems in ANNArchitecture and Learning Approach

Solutions to these two problems leads to an ANNmodel

Two categories of ANN architecture

Feedforward vs. Feedback (Recurrent)

Four Learning StrategiesHebbrain, Error Correction, Winner-take-all,Stochastic


37/102

AI:Summary 37

1x 2x 3x nx

1y

2y

3y

ny

General structures of feedforward networks

General structures of feedback networks


38/102

AI:Summary 38

Multi-layer perceptron is the classical model offeedforward network.

Back-Propagation (B-P) learning algorithm aimsto minimize the Mean Square Error between

generated outputs and desired outputs of thenetwork, which is optimized by Gradient Descent.


39/102

AI:Summary 39

MultiMulti--layer Perceptron (MLP)layer Perceptron (MLP)

Layers are usually fully connected

numbers ofhidden units typically chosen by hand


40/102

AI:Summary 40

BB--P Learning StepsP Learning Steps

Step1. Select a pattern from the training set and present it to the

network.

Step2. Compute activation of input, hidden and output neurons in

that sequence.

Step3. Compute the errorover the output neurons by comparing

the generated outputs with the desired outputs.

Step4. Use the calculated error to update all weights in the

network, such that a global errormeasure gets reduced.

Step5. Repeat Step1 through Step4 until the global error fallsbelow a predefined threshold.


41/102

AI:Summary 41

4.15 Sigmoid

i

BP

iix [,

y

ni ,,1 .!

!

!

n

i

iixy1

tanh [

xx 2tanh1htan !d


42/102

AI:Summary 42

iii

ii tyyEytE !

xx! 2

21

jiji

jiji xg

xg !x

x!

[

[[[

TT

jiiiiji xyty21!xx[

jiiiijiji

jiji xyty21 !

x

x!@ L[

[L[[

1

22 1tanh1tanh iii ygg

ygy !!

x

x! [

[

[T

TT


43/102


44/102

AI:Summary 44

Hopfield network is a classical model offeedbacknetwork. As a dynamic system, Stability is the

main problem of Hopfield network which is solvedby Lyapunov energy function.

Hopfield networks solve problems by going to itsattractor state from any initial state. Its primaryapplications include content addressable memoryand optimization.

1x 2x 3x nx

1y 2y 3y ny


45/102

AI:Summary 45

Self-organizing feature map (SOFM) is a networkwithunsupervised learning, which can be used to

reduce dimensionality withpreservation oftopological information.

Three key components of SOFM algorithm include

competition, cooperation, and adaptation.


46/102

Machine LearningMachine Learning

Chapter 5


47/102

AI:Summary 47

Learning is any process by which a

system improvesperformance fromexperience.

Four elements of intelligent systems withlearning capability

performance, environment, critic, learning


48/102

AI:Summary 48

Learning strategies include rote learning,learning from instruction, learning byanalogy, explanation-based learning,

learning fromind

uction

Rote learning save the pair of solutionsand problem and retrieve them when

needed.


49/102

AI:Summary 49

Explanation-based learning is deductivelearning in essence.

- Input

training example, domain theory, OperationalityCriterion

- Output

target concept

- Two processesExplanation and Generalization


50/102

AI:Summary 50

In supervised learning, a function islearned from the examples ofinput-outputpairs.

Ockham

s razor: prefer t

he simplest

hypot

hesisconsistent with data

Decision tree learning is a method forapproximating discrete functionID3, Information Gain


51/102

AI:Summary 51

BA

A B Result

0 0 0

0 1 0

1 0 1

1 1 0

A

B0

0 1

1 0

0 1

AB:

Result

01note:

5.12

Choose attribute based on informationChoose attribute based on information


52/102

AI:Summary 52

Choose attribute based on informationChoose attribute based on informationtheorytheory

To implement Choose-Attribute in the DTL

algorithm

Information Content (Entropy):

I(P(v1), , P(vn))= i -P(vi) log2 P(vi) For a training set containing p positive

examples and n negative examples:

np

n

np

n

np

p

np

p

np

n

np

pI ! 22 loglog),(


53/102

AI:Summary 53

Information gainInformation gain

A chosen attribute A divides the training set Einto subsets E1, , Ev according to theirvalues for A, where A has v distinct values.

Information Gain (IG) or reduction in entropyfrom the attribute test:

Choose the attribute with the largest IG

!

!v

i ii

i

ii

iii

np

n

np

pI

np

npAremainder

1

),()(

)(),()( Are aindernp

n

np

pIAIG !


54/102

AI:Summary 54

5.13 ABa1a2,TrueF

alse

a1 a2

1 B True False

2 B True True

3 A False True

4 B False False

5 A True False

6 A True True

a1a2

1111


55/102

AI:Summary 55

082.0,918.0

918.03

1log

3

1

3

2log

3

2

918.03

1

log3

1

3

2

log3

2

2

1

2

1

0,1

12

1log

2

1

2

1log

2

1

12

1log

2

1

2

1log

2

13

1

3

2

12

1log

2

1

2

1log

2

1

222

22

22

2

111

22

22

1

22

!!!@

}!

}!

!

!!!@

!!

!!

!

!!

aaa

false

true

falsetruea

aaa

false

true

falsetruea

SGS

S

SGS

S

:

12 aa GG "

a2


56/102

AI:Summary 56

In unsupervised learning, only input datais provided, expected output is unknown.

Clustering is the process ofdividing the

data set into clusters according to thesimilarity between data.

Partition clustering and Hierarchal

clustering are two basic strategies.


57/102

AI:Summary 57

k-means algorithm is a main algorithm ofpartiton clustering, in which each cluster isrepresented by its mean and the data ispartitioned into the closest clusteriteratively.

k-means algorithm is changed into k-mediods algorithm if we represent each

cluster using representative data.


58/102

AI:Summary 58

An example of K-means clustering

From Data

Mining:

Concepts andTechniques, J.

Han and M.

Kamber


59/102

AI:Summary 59

Total

substitution cost

for the data set:

! j jihih CC

Substitution

cost of each

data


60/102

AI:Summary 60

5.23 7

1,2,3,4,5,6,7

1 = 1

2 =

3 =

4

=5 =

6 =

7 = 1

= 877

2127 !C

1402

2

2

5

2

7

3

7 !v PCCC350

3

3

2

3

2

5

2

7

2

4

3

7

4

7 !vvv PCCCCCC

3012

2

2

4

3

7

2

2

3

4

3

7

2

3

4

7

5

7 !vvv PCCPCCCCC

63372

7

1

7 ! CCC


61/102

AI:Summary 61

5.24 D9A1(3,2), A2(3,9), A3(8,6), B1(9,5),

B2(2,4), B3(3,10), C1(2,6), C2(9,6),C3(2,2)

k-Dk=3A1,B1,C1

1

2

3q=3


62/102

AI:Summary 62

1

K1=A1, K2=B1, K3=C1

d(A2, K1) =49 d(A2, K2) =52 d(A2, K3) =10d(A3, K1) =41 d(A3, K2) =2 d(A3, K3) =36

d(B2, K1) =5 d(B2, K2) =50 d(B2, K3) =4

d(B3, K1) =64 d(B3, K2) =61 d(B3, K3) =17

d(C2, K1) =52 d(C2, K2) =1 d(C2, K3) =49

d(C3, K1) =1 d(C3, K2) =58 d(C3, K3) =16

{A1, C3} K1=(2.5, 2)

{B1, A3, C2}, K2=(8.7, 5.7)

{C1, A2, B2, B3}, K3=(2.5, 7.25)


63/102

AI:Summary 63

2

K1, K2, K3

d(A1, K1) =0.25 d(A1, K2) =32.49 d(A1, K3) =27.81

d(A2, K1) =49.25 d(A2, K2) =54.58d(A2, K3) =3.31

d(A3, K1) =46.25 d(A3, K2) =0.58 d(A3, K3) =31.81

d(B1, K1) =51.25 d(B1, K2) =0.58 d(B1, K3) =47.31

d(B2, K1) =4.25 d(B2, K2) =47.78 d(B2, K3) =10.81

d(B3, K1) =64.25 d(B3, K2) =50.98 d(B3, K3) =7.61

d(C1, K1) =16.25 d(C1, K2) =44.98 d(C1, K3) =1.81

d(C2, K1) =58.25 d(C2, K2) =0.18 d(C2, K3) =43.81d(C3, K1) =0.25 d(C3, K2) =58.58 d(C3, K3) =27.81

{A1, B2, C3} K1=(2.3, 2.7)

{B1, A3, C2}, K2=(8.7, 5.7)

{C1, A2, B3}, K3=(2.7, 8.3)


64/102

AI:Summary 64

K1, K2, K3

d(A1, K1) =0.98 d(A1, K2) =32.49 d(A1, K3) =39.78

d(A2, K1) =40.18 d(A2, K2) =54.58d(A2, K3) =0.58d(A3, K1) =43.38 d(A3, K2) =0.58 d(A3, K3) =33.38

d(B1, K1) =50.18 d(B1, K2) =0.58 d(B1, K3) =50.58

d(B2, K1) =1.78 d(B2, K2) =47.78 d(B2, K3) =18.98

d(B3, K1) =53.78 d(B3, K2) =50.98 d(B3, K3) =2.98

d(C1, K1) =10.98 d(C1, K2) =44.98 d(C1, K3) =5.78d(C2, K1) =55.78 d(C2, K2) =0.18 d(C2, K3) =44.98

d(C3, K1) =0.58 d(C3, K2) =58.58 d(C3, K3) =38.29

{A

1, B2, C3}

K1=(2.3, 2.7){B1, A3, C2}, K2=(8.7, 5.7)

{C1, A2, B3}, K3=(2.7, 8.3)


65/102

AI:Summary 65

Two methods ofhierarchal clustering arebottom-up agglomerative nesting and top-

down divisive analysis.


66/102

Nouvelle AINouvelle AI

Chapter 6


67/102

AI:Summary 67

Nouvelle AI Intelligence without representation and reason

Situated AI

Agent Weak notion and strong notion

Architecture types include Deliberative (BDImodel), Reactive(subsumption, agent

network), and Hybrid(PRS, TouringMachines,InteRRaP)


68/102

QQ l C t til C t ti


69/102

AI:Summary 69

r(state, action)immediate reward values Q(state, action) values

V*(state) values

100

0

0

100

G

0

0

0

0

0

0

0

0

0

90

81

100

G

0

81

72

90

81

81

72

90

81

100

G90 100 0

81 90 100

QQ--values Computationvalues Computation

Using the discountedcumulative reward, the discount factor = 0.9

g

!!

0i

it

i

t rsV KT

S! ttt sss ,max* TT

asfVasfasQ tStRt ,,, *K!

81=0+0.9*90


70/102

AI:Summary 70

L i th QL i th Q ll


71/102

AI:Summary 71

Learning the QLearning the Q--valuevalue

Note: Q and V* closely related

Allows us to write Q recursively as

Using Q-values

a's,Qmaxargs*Va'

| asfasfasQ tStRt ,,, *K!

aasfQasfasQ tSa

tRtd!

d,,max,, K

asQs ta

t ,maxarg* !T

A kind ofTemporal Difference (TD) learning

Solve: How

to learn?

Solve: How to

choose the best

action?

QQ L iL i StSt


72/102

AI:Summary 72

QQ--LearningLearning StepsSteps

FOR each DO Initialize table entry:

Observe current state s

WHILE (true) DO

Selectaction a and execute it Receive immediate reward r

Observe new state s

Update table entry for as follows

Move: record transition from s to s

0nas,Q

a,

a','axa,ra,a'

n


73/102

AI:Summary 73

R Matrix

Initial Q

Q Matrix

Final Q


74/102

Evolutionary ComputationEvolutionary Computation

Chapter 7


75/102

AI:Summary 75

Evolutionary Computation is based on biologicalmetaphor:

Optimized Behavior Optimal Solution

Individual Solution;Fitness Objective; Environment Problem

Ingredients of EC

Representation: Individual and Population

Fitness evaluation: Objective

Selection: Parent and Survivor

Genetic Operators: Mutation and/or Recombination Initialization/Termination

Th I di tTh I di t


76/102

AI:Summary 76

The IngredientsThe Ingredients

t t + 1

mutationrecombination

reproduction

selection

Image from Ida Sprinkhuizen-Kuyper: Introduction to

Evolutionary Computation, 2000.

The Evolution MechanismThe Evolution Mechanism


77/102

AI:Summary 77

The Evolution MechanismThe Evolution Mechanism

Increasing diversity by genetic operators

mutation

Recombination

Decreasing diversity by selection

of parents

of survivors

Th E l ti C lTh E l ti C l


78/102

AI:Summary 78

The Evolutionary CycleThe Evolutionary Cycle

Recombination

MutationPopulation

Offspring

ParentsParent Selection

Survivor Selection

Image from Ben Paechter: Evolutionary Computing

A Practical Introduction

Desirable Performance of ECDesirable Performance of EC


79/102

AI:Summary 79

Desirable Performance of ECDesirable Performance of EC

Image from: Introduction to Stochastic Search and Optimization (ISSO) by J. C. Spall


80/102

AI:Summary 80

Main Features of EC: Parallel and Stochastic

Easy applicable

Exploration/Exploitation Balance

A key issue in EC Main streams of Evolutionary Algorithms

Genetic Algorithm

Evolutionary Programming

Evolutionary Strategies

Genetic Programming


81/102

AI:Summary 81

Genetic Algorithm Representation: Bit-string (Genotype)

Mutation: Bit-swap

Recombination:

Crossover

ParentSelection: Chance according toFitness

SurvivorSelection: Extinct Replacement

Worked ExampleWorked Example


82/102

AI:Summary 82

Worked ExampleWorked Example

World size: 4X4

Population size: N= 4

Genome: 16 bits

Fitness:

F(p)= squares from goal

Selection: Greedy

Example: Initial PopulationExample: Initial Population


83/102

AI:Summary 83

Example:Initial PopulationExample:Initial Population

Initial Population P(0): 4 random 16-bitstrings

Example: Fitness CalculationExample: Fitness Calculation


84/102

AI:Summary 84

Example: Fitness CalculationExample: Fitness Calculation

F(p1)=(8-8) 4 = -4 F(p2)= -5 F(p3)= -6 F(p4)= -4

Example: Selection and ChangeExample: Selection and Change


85/102

AI:Summary 85

Example: Selection and ChangeExample: Selection and Change

Select p1 and p4 as parents of the nextgeneration

Produce offspring using crossover and mutation

Example: Survivor SelectionExample: Survivor Selection


86/102

AI:Summary 86

Example: Survivor SelectionExample: Survivor Selection

The next generation is...

Example: Repeat for next GenerationExample: Repeat for next Generation


87/102

AI:Summary 87

Example: Repeat for next GenerationExample: Repeat for next Generation

Repeat: F(p1)= -4 F(p2)= -4 F(p3)=0 F(p4)= -4


88/102

AI:Summary 88

7.1 A*

1 A*2A*


89/102

Swarm IntelligenceSwarm Intelligence

Chapter 8


90/102

AI:Summary 90

Swarm Intelligence (SI) an artificial intelligence technique based

around the study ofcollective behavior indecentralized, self-organized systems.

Multi-Agent Systems (MAS)

A system that consists of a number of agents,whichinteract with one-another.

Communication, Coordination,Collaboration


91/102

AI:Summary 91

AntColony Optimization (A

CO)

Inspired by ant colony foraging

pheromone as heuristic information(stigmergy)

Iteration between Construct

AntSolutions andUpdatePheromones

Particle Swarm Optimization (PSO)

Inspired by bird flocking Heuristic information: results from partners

Particle Velocity Update


92/102

AI:Summary 92

Since the route B isshorter, the ants on thispath will complete the

travel more times andthereby lay morepheromone over it.

The pheromoneconcentration on trail B willincrease at a higher ratethan on A, and soon theants on route A will chooseto follow route B

Since most ants will nolonger travel on route A,and since the pheromone isvolatile, trail A will startevaporating

Only the shortest routewill remain!

Model Ant Colony for OptimizationModel Ant Colony for Optimization


93/102

AI:Summary 93


Eachartificial ant is a probabilistic mechanism thatconstructs a solution to the problem, using:

Artificial pheromone deposition

Heuristic information: pheromone trails, and others

Differences between real

and artificial ants:

The pheromone is updated

only after a solution has

been constructed.

Additional mechanisms



94/102

AI:Summary 94

(Cont(Contd)d)

ConstructAntSolutions The rules for the probabilistic choice of solution

components.

UpdatePheromones

It is used to increase the pheromone values associated with

good or promising solutions, and decrease those that areassociated with bad ones.

Decreasing all the pheromone values throughpheromoneevaporation -->allows forgetting-> favors exploration ofnew areas

Increasing the pheromone levels associated with a chosen

set of good solutions -> makes the algorithm converge to asolution


95/102

AI:Summary 95

Illustrating the velocity update schema of global PSO


96/102

Next Generation ofNext Generation of

ComputersComputers

Chapter 11

Optical ComputersOptical Computers


97/102

AI:Summary 97

Optical ComputersOptical Computers

Optical Computers perform computations,operate, store and transmit data usingonly light.

Two kinds of optical computers

Electro-Optical Hybrid and Pure Optical

Optical logic and Holographic Memory

Optical Interconnect

optical fibers, waveguides and free-spaceoptics.

DNA ComputDNA Computinging


98/102

AI:Summary 98

DNA ComputDNA Computinging

DNA itself does not carry out any computation. It

rather acts as a massive memory.

BUT, the way complementary bases react with each

other can be used to compute things. Proposed by Adelman in 1994 to solve Hamiltonian

Path Problem with DNA.

Uniqueness of DNA

- Extremely dense information storage.

- Enormous parallelism.

Quantum ComputingQuantum Computing


99/102

AI:Summary 99

Quantum ComputingQuantum Computing

A quantum computer is a machine that

performs calculations based on the laws

ofquantum mechanics, which is thebehavior of particles at the sub-atomic

level.


100/102

AI:Summary 100

Have you got overall understanding ofAI, and prepared yourself for learning

and study of the branches of AI?

Course GradingCourse Grading


101/102

AI:Summary 101

gg

Three components

- Final exam70%

2009.11.2510:10 - 12:10, 2008

- Programming 20%

2.10, 3.11, 5.17, 8.4

Submitted before 2009.1.20

- Problem sets (closed)10%


102/102

Questions?Questions?

E_mail: [email protected]

Tel: 68913447 (office)

Documents

Teaching AI Undergraduate 10.Summary