15 puzzle problem using branch and bound
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The document discusses the branch and bound algorithm for solving the 15-puzzle problem. It describes the key components of branch and bound including live nodes, e-nodes, and dead nodes. It also defines the cost function used to evaluate nodes as the sum of the path length and number of misplaced tiles. The algorithm generates all possible child nodes from the current node and prunes the search tree by comparing node costs to avoid exploring subtrees without solutions.
15 puzzle problem using branch and bound
- 1. 15 PUZZLE PROBLEM USING BRANCH AND BOUND CREATED BY:- ABHISHEK KUMAR SINGH
- 2. • Branch and Bound • The search for an answer node can often be speeded by using an “intelligent” ranking function, also called an approximate cost function to avoid searching in sub-trees that do not contain an answer node. It is similar to backtracking technique but uses BFS-like search. • There are basically three types of nodes involved in Branch and Bound • 1. Live node is a node that has been generated but whose children have not yet been generated. • 2. E-node is a live node whose children are currently being explored. In other words, an E-node is a node currently being expanded. • 3. Dead node is a generated node that is not to be expanded or explored any further. All children of a dead node have already been expanded.
- 3. • Cost function: • Each node X in the search tree is associated with a cost. The cost function is useful for determining the next E-node. The next E-node is the one with least cost. The cost function is defined as, • C(X) = g(X) + h(X) where g(X) = cost of reaching the current node from the root h(X) = cost of reaching an answer node from X • Ideal Cost function for 15-puzzle Algorithm : • We assume that moving one tile in any direction will have 1 unit cost. Keeping that in mind, we define cost function for 15- puzzle algorithm as below : • c(x) = f(x) + h(x) where f(x) is the length of the path from root to x (the number of moves so far) and h(x) is the number of non-blank tiles not in their goal position (the number of mis- - placed tiles). There are at least h(x) moves to transform state x to a goal state
- 4. 1111 2@ 3 4 5 6 8 9 10 7 11 13 14 15 12 1111 2@ 33 4 5 6 7 8 9 10 11 12 13 14 15 Initial arrangement Goalarrangement
- 5. 1 2 3 4 5 6 8 9 10 7 11 13 14 15 12 up right left down 1 2 4 5 6 3 8 9 10 7 11 13 14 15 12 1 2 3 4 5 6 8 9 10 7 11 13 14 15 12 1 2 3 4 5 6 7 8 9 10 11 13 14 15 12 1 2 3 4 5 6 8 9 10 7 11 13 14 15 12 C=1+4 C=1+4 C=1+2 C=1+4
- 6. 1 2 4 5 6 3 8 9 10 7 11 13 14 15 12 1 2 3 4 5 6 8 9 10 7 11 13 14 15 12 1 2 3 4 5 6 7 8 9 10 11 13 14 15 12 1 2 3 4 5 6 8 9 10 7 11 13 14 15 12 1 2 3 4 5 6 7 8 9 10 11 13 14 15 12 1 2 3 4 5 6 7 8 9 10 15 11 13 14 12 1 2 3 4 5 6 7 8 9 10 11 13 14 15 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 13 14 15 12 C=2+1 C=2+3 C=3+0C=3+2 C=2+3
- 7. • ALGORITHM 15-PUZZLE PROBLEM • struct list_node • { • list_node *next; • list_node *parent; • float cost; • }; • algorithm LCSearch(list_node *t) • { • if (*t is an answer node) • { • print(*t); • return; • } • E =t; • Initialize the list of live nodes to be empty;
- 8. • while (true) { • for each child x of E • { • if x is an answer node { • print the path from x to t; • return; } • Add (x); • x->parent = E; } • if there are no more live nodes • { • print ("No answer node"); • return; • } • E = Least(); • } • } •