Artificial Intelligence Lecture

April 19, 2023 Artificial Intelligence, Lecturer #14

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Artificial IntelligenceArtificial IntelligenceLectureLecture

Md. Morshedul Islam Assistant Professor

Department of Computer Science & EngineeringBangladesh University of Business and Technology (BUBT)


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ContentsContents

Informed Search Strategies Heuristic Information Hill Climbing Methods Best-First Search Optimal Search and A* Branch-and-Bound Search Iterative Deepening A* Memory-bounded Heuristic Search


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Hill Climbing Methods:Hill Climbing Methods:An ExampleAn Example

21

9

1916

S

28 241821

11 22 20

23

27

25

2525

16 1923

25 25 25


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Best-First Search: An Best-First Search: An ExampleExample

g

181615

S

12

282220

10 8 2

12

8

18

12

25 2220

15 10 55 10

L1: S20, S22, S28L2: S35, S36, S38L3: S40, S45


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Optimal Search and AOptimal Search and A**

The A* algorithm is a specialization of best-first-search It provides general guidelines with which to estimate goal

distances for general search graphs. At each node along a path to the goal, the A* algorithm

generates all successors nodes and computes the distance (cost) from the start node to a goal node through each of the successor.

It then chooses the successor with the shortest distance for expansion.

The successor for this node are then generated. Node are labeled with f(n) = g(n)+h(n)


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Optimal Search and AOptimal Search and A**: : ExampleExampleRoad Map of a CityRoad Map of a CityO

Z

A

I

T

M

D

L

FS

B

P

C

N

R

G

V

E

HU

71

75

118

111

70

75120

151

14099

80

146

21197

138

101

90

85

87

92

142

98

86


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Optimal Search and AOptimal Search and A**: : ExampleExample

Values of hSLD-straight-line distance to B

A 366 H 151 R 193

B 0 I 226 S 253

C 160 L 244 T 329

D 242 M 241 U 80

E 161 N 234 V 199

F 176 O 380 Z 374

G 77 P 100


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Optimal Search and AOptimal Search and A**: : ExampleExample Node are labeled with Node are labeled with f(n) = g(n)+h(n)f(n) = g(n)+h(n)

(a) Initial State: A

366+0

(b) Expanding A: A

393=140+253

S T Z

447=118+329 449=75+374


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(c) Expanding S: A

646=280+366,

S T Z447=118+329 449=75+374

RFA O415=239+176,

671=291+380 413=220+193

140

99140

151 80


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(d) Expanding R: A

646=280+366,

S T Z447=118+329 449=75+374

RFA O415=239+176,

671=291+380

417=317+100

140

99140

151 80

SPC146

9780

526=366+160 553=300+253


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(e) Expanding F: A

646=280+366,

S T Z447=118+329 449=75+374

RFA O

591=338+253

671=291+380

417=317+100

140

99140

151 80

SPC146

9780

526=366+160553=300+253

BS

21199

450=450+0


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Optimal Search and AOptimal Search and A**: : ExampleExample

526=366+160

(f) Expanding P: A

646=280+366,

S T Z447=118+329 449=75+374

RFA O

591=338+253

671=291+380

140

99140

151 80

SPC146

9780

553=300+253BS

21199

450=450+0

138

B C R418=418+0

10197

615=455+160 607=414+193


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Greedy Best-first SearchGreedy Best-first Search: : ExampleExample

Node are labeled with Node are labeled with f(n) =h(n)f(n) =h(n)

A

S T Z

RFA O

253

329

366

176

253

380 193

374

BS

0

366


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Branch-and-Bound SearchBranch-and-Bound Search

This strategy saves all paths lengths (or costs) from a node to all generated nodes and chooses the shortest path for farther expansion.

It then compares the new path length with all old ones and again chose the shortest path for expansion.

In this method, a lowest cost path will be found. Expensive in case of computing and remembering

all computing paths

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Branch-and-Bound Search: Branch-and-Bound Search: ExampleExample

L1: S12, S14, S15

L2: S12+11, S12+14, S12+12; S14+11, S14+9; S15+13, S15+8, S15+11L3: S23+12, S23+13, S26+13, S26+9, S24+9, S24+10; S25+10, S25+9, S23+9; S28+9, S28+15, S23+1, S26+1, S26+5

121411

S

9

151412

13 9 10

11

10

9

9

8 1113

15 1 512 1

G


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Recommended TextbooksRecommended Textbooks

[Negnevitsky, 2001] M. Negnevitsky “ Artificial Intelligence: A guide to Intelligent Systems”, Pearson Education Limited, England, 2002.

[Russel, 2003] S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition

[Patterson, 1990] D. W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice-Hall Inc., Englewood Cliffs, N.J, USA, 1990.

[Minsky, 1974] M. Minsky “A Framework for Representing Knowledge”, MIT-AI Laboratory Memo 306, 1974.


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End of PresentationEnd of Presentation

Questions or Suggestions?

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Artificial Intelligence Lecture

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