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Iterative Deepening A*
Algorithm
(Extension of A*)
Lecture-17
Hema Kashyap
1
Introduction
• Iterative deepening A* or IDA* is similar to
iterative-deepening depth-first, but with the
following modifi...
Iterative Deepening Search
• Iterative Deepening is a kind of uniformed
search strategy
• Combines the benefits of depth-f...
IDA*(Iterative Deepening A*) Search
• Perform depth-first search LIMITED to some f-
bound.
• If goal found: ok.
• Else: in...
Example
S
f=100
A
f=120
B
f=130
C
f=120
D
f=140
G
f=125
E
f=140
F
f=125
f-new = 120f-limited, f-bound = 100
5
Example
S
f=100
A
f=120
B
f=130
C
f=120
D
f=140
G
f=125
E
f=140
F
f=125
f-limited, f-bound = 120 f-new = 125
6
Example
S
f=100
A
f=120
B
f=130
C
f=120
D
f=140
G
f=125
E
f=140
F
f=125
f-limited, f-bound = 125
SUCCESS
7
IDA* Analysis
• IDA* is complete, optimal,
and optimally efficient
(assuming a consistent,
admissible heuristic), and
requ...
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Lecture 17 Iterative Deepening a star algorithm

Extension of A* Algorithm

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Lecture 17 Iterative Deepening a star algorithm

  1. 1. Iterative Deepening A* Algorithm (Extension of A*) Lecture-17 Hema Kashyap 1
  2. 2. Introduction • Iterative deepening A* or IDA* is similar to iterative-deepening depth-first, but with the following modifications: • The depth bound modified to be an f-limit 1. Start with limit = h(start) 2. Prune any node if f(node) > f-limit 3. Next f-limit=minimum cost of any node pruned 2
  3. 3. Iterative Deepening Search • Iterative Deepening is a kind of uniformed search strategy • Combines the benefits of depth-first and breadth- first search • Advantage- – it is optimal and complete like breadth first search – Modest memory requirement like depth-first search 3
  4. 4. IDA*(Iterative Deepening A*) Search • Perform depth-first search LIMITED to some f- bound. • If goal found: ok. • Else: increase de f-bound and restart. • How to establish the f-bounds? • - initially: f(S) • generate all successors • record the minimal f(succ) > f(S) • Continue with minimal f(succ) instead of f(S) f4 f3 f2 f1 4
  5. 5. Example S f=100 A f=120 B f=130 C f=120 D f=140 G f=125 E f=140 F f=125 f-new = 120f-limited, f-bound = 100 5
  6. 6. Example S f=100 A f=120 B f=130 C f=120 D f=140 G f=125 E f=140 F f=125 f-limited, f-bound = 120 f-new = 125 6
  7. 7. Example S f=100 A f=120 B f=130 C f=120 D f=140 G f=125 E f=140 F f=125 f-limited, f-bound = 125 SUCCESS 7
  8. 8. IDA* Analysis • IDA* is complete, optimal, and optimally efficient (assuming a consistent, admissible heuristic), and requires only a polynomial amount of storage in the worst case: • IDA* is complete & optimal Space usage is linear in the depth of solution. Each iteration is depth first search, and thus it does not require a priority queue. 8

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