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Extension of A* Algorithm

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- 1. Iterative Deepening A* Algorithm (Extension of A*) Lecture-17 Hema Kashyap 1
- 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. 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. 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. 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. 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. 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. 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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