ailast.pptx - Informed Search(\u2026\u2026.continued from chapte three \u2022 Section Objectives \u2013 Define Informed search algorithms(strategies \u2013

# ailast.pptx - Informed Search(…….continued from chapte...

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Section Objectives Define Informed search algorithms(strategies) Differentiate between Blind and Informed search Identify types of Informed Search Best-first search Memory Bound Best First search Iterative improvement algorithm (Local search algorithms) Understand the use of an evaluation function f(n) Understanding Admissible heuristics 1 Informed Search(…….continued from chapte three 07/06/2019 AI/CSE 3206
2 Informed ….contd Informed search is a strategy that uses information about the cost that may be incurred to achieve the goal state from the current state. The information may not be accurate. But it will help the agent to make better decision This information is called heuristic information 07/06/2019 AI/CSE 3206
Informed ….contd There several algorithms that belongs to this group. Some of these are: Best-first search 1. Greedy best-first search 2. A * search Memory Bound Best First search 1. Iterative deepening A* (IDA*) search Iterative improvement algorithm (Local search algorithms) 1. Hill-climbing search 2. Simulated annealing search 07/06/2019 3 AI/CSE 3206
4 Informed ….contd Best-first search Idea: use an evaluation function f(n) for each node Estimate of "desirability“ using heuristic and path cost Expand most desirable unexpanded node(Expand the node n with smallest f(n)) The information gives a clue about which node to be expanded first This will be done during queuing The best node according to the evaluation function may not be best Implementation : Order the nodes in fringe in decreasing order of desirability (increasing order of cost evaluation function) 07/06/2019 AI/CSE 3206
Informed ….contd Greedy Best-First Search 1. Put the initial node on a list START 2. If (START is empty) or (START =GOAL) terminate search 3. Remove the first node form START. Call this node n . 4. If ( n = GOAL) terminate search with success. 5. Else if node n has successor, generate all of them. Find out how far the goal node. Sort all the children generated so far by the remaining distance from the goal.Name this list as START1 6. Replace START with START1 7. Go to Step 2 . 07/06/2019 5 AI/CSE 3206
Informed ….contd f(n)= h(n) # of nodes tested 1, expanded 1 S h=8 B h=4 G h=0 C h=3 A h=8 D h= E h= 1 8 5 9 4 5 3 7 Expanded Node OPEN list (S:8) S not goal (C:3,B:4,A:8) eedy Best-First Search 07/06/2019 6 AI/CSE 3206
Informed ….contd f(n)= h(n) # of nodes tested 2, expanded 2 S h=8 B h=4 G h=0 C h=3 A h=8 D h= E h= 1 8 5 9 4 5 3 7 Expanded Node OPEN list (S:8) S (C:3,B:4,A:8) C not goal (G:0,B:4,A:8) eedy Best-First Search 07/06/2019 7 AI/CSE 3206
Informed ….contd f(n)= h(n) # of nodes tested 3, expanded 2 S h=8 B h=4 G h=0 C h=3 A h=8 D h= E h= 1 8 5 9 4 5 3 7 Expanded Node OPEN list (S:8) S (C:3,B:4,A:8) C (G:0,B:4,A:8) G goal (B:4.A:8) no expansion eedy Best-First Search 07/06/2019 8 AI/CSE 3206
Informed ….contd f(n)= h(n) # of nodes tested 3, expanded 2 S h=8 B h=4 G h=0 C h=3 A h=8 D h= E h= 1 8 5 9 4 5 3 7 Expanded Node OPEN list (S:8) S (C:3,B:4,A:8) C (G:0,B:4,A:8) G goal (B:4.A:8)

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• Summer '19
• Logic, Test, First-order logic, A* search algorithm

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