![• In this search example, we are using two lists which are OPEN and CLOSED Lists. • Expand the nodes of S and put in the CLOSED list • Initialization: Open [A, B], Closed [S] • Iteration 1: Open [A], Closed [S, B] • Iteration 2: Open [E, F, A], Closed [S, B] : Open [E, A], Closed [S, B, F] • Iteration 3: Open [I, G, E, A], Closed [S, B, F] : Open [I, E, A], Closed [S, B, F, G] • Hence the final solution path will be: S----> B----->F----> G Our website:- pywix.blogspot.com Rahul Gupta Sir Video By Digital Classes](https://image.slidesharecdn.com/bestfirstsearchalgorithminartificialintelligence-200502133946/75/Best-first-search-algorithm-in-Artificial-Intelligence-8-2048.jpg)
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Best first search algorithm in Artificial Intelligence
The document discusses the best-first search algorithm, which is a combination of depth-first and breadth-first search. It selects the most promising path at each step based on an estimated heuristic cost function. The algorithm maintains OPEN and CLOSED lists, adding successors of expanded nodes to OPEN and moving nodes to CLOSED after expansion. It terminates if it finds the goal node or the OPEN list is empty. An example traces the steps of applying best-first search to a problem space.
Best first search algorithm in Artificial Intelligence
- 1. Artificial Intelligence Tutorial Best-firstSearch Algorithm (Greedy Search) Our website:- pywix.blogspot.com Rahul Gupta Sir Video by Digital Classes
- 2. Introduction • Greedy best-firstsearch algorithm always selects the path which appears best at that moment. It is the combination of depth-first search and breadth-first search algorithms. It uses the heuristic function and search. Best- first search allows us to take the advantages of both algorithms. Our website:- pywix.blogspot.com Rahul Gupta Sir Video by Digital classes
- 3. Introduction • With thehelp of best-first search, at each step, we can choose the most promising node. In the best first search algorithm, we expand the node which is closest to the goal node and the closest cost is estimated by heuristic function, i.e. • f(n)= h(n). • Were, h(n)= estimated cost from node n to the goal. Our website:- pywix.blogspot.com Rahul Gupta Sir Video by Digital classes
- 4. Best first searchalgorithm • Step 1: Place the starting node into the OPEN list. • Step 2: If the OPEN list is empty, Stop and return failure. • Step 3: Remove the node n, from the OPEN list which has the lowest value of h(n), and places it in the CLOSED list. • Step 4: Expand the node n, and generate the successors of node n. Our website:- pywix.blogspot.com Rahul Gupta Sir Video by Digital classes
- 5. Best first searchalgorithm • Step 5: Check each successor of node n, and find whether any node is a goal node or not. If any successor node is goal node, then return success and terminate the search, else proceed to Step 6. • Step 6: For each successor node, algorithm checks for evaluation function f(n), and then check if the node has been in either OPEN or CLOSED list. If the node has not been in both list, then add it to the OPEN list. • Step 7: Return to Step 2. Our website:- pywix.blogspot.com Rahul Gupta Sir Video by Digital classes
- 6. Our Website:- Pywix.blogspot.com RahulGupta Sir Video by Digital classes Example
- 7. Our website:- pywix.blogspot.com Rahul GuptaSir Video by Digital classes
- 8. • In thissearch example, we are using two lists which are OPEN and CLOSED Lists. • Expand the nodes of S and put in the CLOSED list • Initialization: Open [A, B], Closed [S] • Iteration 1: Open [A], Closed [S, B] • Iteration 2: Open [E, F, A], Closed [S, B] : Open [E, A], Closed [S, B, F] • Iteration 3: Open [I, G, E, A], Closed [S, B, F] : Open [I, E, A], Closed [S, B, F, G] • Hence the final solution path will be: S----> B----->F----> G Our website:- pywix.blogspot.com Rahul Gupta Sir Video By Digital Classes