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Heuristic_Search_Part2

Heuristic_Search_Part2 - Heuristic(Informed Search R&N Chap...

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1 Heuristic (Informed) Search R&N: Chap. 4, Sect. 4.1–3
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2 Iterative Deepening A* (IDA*) Ide a: Re duc e  m e m o ry re q uire m e nt o f A*  b y applying  c uto ff o n va lue s  o f f C o ns is te nt he uris tic  func tio n h Alg o rithm  IDA*: 1. Initia lize  c uto ff to  f(initia l-no de ) 2. Re pe a t: a. Pe rfo rm  de pth-firs t s e a rc h b y e xpa nding  a ll  no de s  N s uc h tha t f(N)   c uto ff b. Re s e t c uto ff to  s m a lle s t va lue  f o f no n-e xpa nde d  (le a f) no de s
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3 8-Puzzle 4 6 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s C uto ff= 4
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4 8-Puzzle 4 4 6 C uto ff= 4 6 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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5 8-Puzzle 4 4 6 C uto ff= 4 6 5 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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6 8-Puzzle 4 4 6 C uto ff= 4 6 5 5 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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7 4 8-Puzzle 4 6 C uto ff= 4 6 5 5 6 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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8 8-Puzzle 4 6 C uto ff= 5 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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9 8-Puzzle 4 4 6 C uto ff= 5 6 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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10 8-Puzzle 4 4 6 C uto ff= 5 6 5 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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11 8-Puzzle 4 4 6 C uto ff= 5 6 5 7 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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12 8-Puzzle 4 4 6 C uto ff= 5 6 5 7 5 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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13 8-Puzzle 4 4 6 C uto ff= 5 6 5 7 5 5 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s
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14 8-Puzzle 4 4 6 C uto ff= 5 6 5 7 5 5 f(N) = g (N) + h(N)     with h(N) = num b e r o f  m is pla c e d tile s 5
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15 Advantages/Drawbacks of IDA* Adva ntag e s : S till c o m ple te  a nd o ptim a l Re q uire s   le s s  m e m o ry  tha n A* Avo id the  o ve rhe a d to  s o rt the  fring e Dra wb a c ks : C a n’t a vo id re vis iting  s ta te s  no t o n the  c urre nt  pa th Es s e ntia lly a  DFS Ava ila b le  m e m o ry is  po o rly us e d  (  m e m o ry-b o unde d s e a rc h, s e e  R&N p. 101-104)
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16 Another approach… Local Search Algorithms Hill-climbing or Gradient descent Simulated Annealing Genetic Algorithms, others…
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Heuristic_Search_Part2 - Heuristic(Informed Search R&N Chap...

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