CS.462Artificial Intelligence

SOMCHAI THANGSATHITYANGKUL

Lecture 03 : Search (2)

2

Uniform Cost Search (UCS)

• Uniform Cost Search:– Nodes in agenda keep track of total

path length from start to that node– Agenda kept in priority queue ordered

by path length– Get shortest path in queue

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Uniform Cost Search (UCS)

Graph:

2

4

UCSAgenda:

Expand node Nodes list

s

{s}

{Bs :2,Cs:4 ,As:5 }

5

Try thisFind a path from node A to the goal

node B. Use UCS method.

AB

Z

OS F

C

PR

T L MD

118

75

71 151

140

111 70 75

120

90

99

211

97

146 138

101

UCS

6

S140 O146 L229

A

T118 S140 O146

Z75 T118 S140A

B

ZO

S F

C

PR

T L MD

118

75

71 151

140

111 7075

120

90

99

211

97

146 138

101

7

Informed Search

• If we use uniform cost search on this graph

What’s wrong ?

8

Informed Search

• Informed Search or Heuristic Search methods try to estimate the “distance” to a goal state. A heuristic function h(s) is the estimated distance to the goal state.

• - Best first greedy search - use h(s) instead of g(s) - Always expand the node with the least h(s) - Use a priority queue: - - Push in states with their second half cost h(s)

Pop out the state with the least h(s) first.

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- Best first greedy search

Graph:

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- Best first greedy searchAgenda:

Expand node Nodes list

s

{s:13}

{Bs:10, As:12,Cs :15 }

11

Try thisUse Best first greedy search path from Arad to Buchraest

12

A* Search

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A* Search

• Evaluation function f(n)= g(n)+h(n)– g(n) the cost (from the start) to reach th

e node n.– h(n) estimated cost to get from the nod

e n to the goal.– f(n) estimated total cost of path throug

h n to goal.

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A* Search

Graph:

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A* Search

Agenda:

Expand node Nodes list

s

{s:13}

{Bs:12, As:17,Cs :19 }

16

Try thisUse A* search path from Arad to Buchraest