AND-OR search
With Example
15
AND-OR Search (AO*)
 AND/OR graph is a form of graph or tree
used in problem solving and problem
decomposition. The nodes of
the graph represent states or goals and their
successors are labeled as either AND or OR
branches.
 The AND successors are subgoals that must
all be achieved to satisfy the parent goal,
while OR branches indicate alternative
subgoals, any one of which could satisfy the
parent goal.
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A
D
C
B
1
1
1
6 5
3
AND
OR
f(n)=g(n)+h(n)
f(n)=cost function
g(n)= Actual cost or edge value.
h(n)= heuristic value
Path-1 (AB) 1+6=7
Path-2 (ACD) 1+3+1+5=10
7 10
AB
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A
B D
C
F
E H
G J
I
1
1
1
1
1
1 1 1 1
15 8 7 1 6 3
10
13
20
f(n)=g(n)+h(n)
Path1: f(AB)=1+20=21
Path2: f(ACD)=1+13+1+10=25
f(BEF)= 1+15+1+8= 25
f(AB)=1+25=26 (revised)
f(DIJ)=1+6+1+3= 11
f(CGH)= 1+7+1+1=10
f(ACD)= 1+10+1+11=23 (revised)
25
min(26,23)=23
11
10
21 25
26 23
Difference between A* and AO*
A* algorithm
 A* algorithm is OR graph
algorithm.
 It just find only one solution.
 A* algorithm guarantees to give
an optimal solution.
AO* algorithm
 AO* is a AND-OR graph algorithm.
 It find more than one solution by
ANDing two or more branches.
 AO* algorithm does not always give
optimum solution. AO* doesn't
explore all other solutions once it got
a solution.
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Reference:
Artificial Intelligence
A Modern Approach Third Edition
Peter Norvig and Stuart J. Russell
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And or search

  • 1.
  • 2.
    AND-OR Search (AO*) AND/OR graph is a form of graph or tree used in problem solving and problem decomposition. The nodes of the graph represent states or goals and their successors are labeled as either AND or OR branches.  The AND successors are subgoals that must all be achieved to satisfy the parent goal, while OR branches indicate alternative subgoals, any one of which could satisfy the parent goal. Subscribe Goal: Acquired Mobile Goal: Buy a Mobile Goal: Earn some money Goal: Steal Mobile
  • 3.
    Subscribe A D C B 1 1 1 6 5 3 AND OR f(n)=g(n)+h(n) f(n)=cost function g(n)=Actual cost or edge value. h(n)= heuristic value Path-1 (AB) 1+6=7 Path-2 (ACD) 1+3+1+5=10 7 10 AB
  • 4.
    Subscribe A B D C F E H GJ I 1 1 1 1 1 1 1 1 1 15 8 7 1 6 3 10 13 20 f(n)=g(n)+h(n) Path1: f(AB)=1+20=21 Path2: f(ACD)=1+13+1+10=25 f(BEF)= 1+15+1+8= 25 f(AB)=1+25=26 (revised) f(DIJ)=1+6+1+3= 11 f(CGH)= 1+7+1+1=10 f(ACD)= 1+10+1+11=23 (revised) 25 min(26,23)=23 11 10 21 25 26 23
  • 5.
    Difference between A*and AO* A* algorithm  A* algorithm is OR graph algorithm.  It just find only one solution.  A* algorithm guarantees to give an optimal solution. AO* algorithm  AO* is a AND-OR graph algorithm.  It find more than one solution by ANDing two or more branches.  AO* algorithm does not always give optimum solution. AO* doesn't explore all other solutions once it got a solution. Subscribe
  • 6.
    Thanks For Watching Reference: Artificial Intelligence AModern Approach Third Edition Peter Norvig and Stuart J. Russell Subscribe Like Share Next Topic: Minimax Algorithm
  • 7.
    OMega TechEd About theChannel This channel helps you to prepare for BSc IT and BSc computer science subjects. In this channel we will learn Business Intelligence ,Artificial Intelligence, Digital Electronics, Internet OF Things Python programming , Data-Structure etc. Which is useful for upcoming university exams. Gmail: omega.teched@gmail.com Social Media Handles: omega.teched megha_with Subscribe