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## Depth First Search

Depth First Search (DFS) searches deeper into the problem space. Breadth-first search always generates successor of the deepest unexpanded node. It uses last-in first-out stack for keeping the unexpanded nodes. More commonly, depth-first search is implemented recursively, with the recursion stack taking the place of an explicit node stack.

### Algorithm: Depth First Search

1.If the initial state is a goal state, quit and return success.
2.Otherwise, loop until success or failure is signaled.
a) Generate a state, say E, and let it be the successor of the initial state. If there is no successor, signal failure.
b) Call Depth-First Search with E as the initial state.
c) If success is returned, signal success. Otherwise continue in this loop.

• The advantage of depth-first Search is that memory requirement is only linear with respect to the search graph. This is in contrast with breadth-first search which requires more space. The reason is that the algorithm only needs to store a stack of nodes on the path from the root to the current node.

• The time complexity of a depth-first Search to depth d is O(b^d) since it generates the same set of nodes as breadth-first search, but simply in a different order. Thus practically depth-first search is time-limited rather than space-limited.

• If depth-first search finds solution without exploring much in a path then the time and space it takes will be very less.

• The disadvantage of Depth-First Search is that there is a possibility that it may go down the left-most path forever. Even a finite graph can generate an infinite tree. One solution to this problem is to impose a cutoff depth on the search. Although the ideal cutoff is the solution depth d and this value is rarely known in advance of actually solving the problem. If the chosen cutoff depth is less than d, the algorithm will fail to find a solution, whereas if the cutoff depth is greater than d, a large price is paid in execution time, and the first solution found may not be an optimal one.

• Depth-First Search is not guaranteed to find the solution.

• And there is no guarantee to find a minimal solution, if more than one solution exists.

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# Uniform-Cost Search Algorithm

If all the edges in the search graph do not have the same cost then breadth-first search generalizes to uniform-cost search. Instead of expanding nodes in order of their depth from the root, uniform-cost search expands nodes in order of their cost from the root. At each step, the next step n to be expanded is one whose cost g(n) is lowest where g(n) is the sum of the edge costs from the root to node n. The nodes are stored in a priority queue. This algorithm is also known as Dijkstra’s single-source shortest algorithm.

Whenever a node is chosen for expansion by uniform cost search, a lowest-cost path to that node has been found. The worst case time complexity of uniform-cost search is O(bc/m), where c is the cost of an optimal solution and m is the minimum edge cost. Unfortunately, it also suggests the same memory limitation as breadth-first search.

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Depth First Search

# Depth-First Iterative Deepening (DFID) Search

Depth-First Iterative Deepening (DFID) search combines the best features of breadth-first search and depth-first search. Depth-First Iterative Deepening search first performs a depth-first search to depth one, then starts over, executing a complete depth-first search to depth two, and continues to run depth-first searches to successively greater depths, until a solution is found.

Since it never generates a node until all shallower nodes have been generated, the first solution found by Depth First Iterative Deepening search is guaranteed to be along a shortest path. Moreover, since at any given point it is executing a depth-first search, saving only a stack of nodes, and the algorithm terminates when it finds a solution at depth d, the space complexity of DFID is only O(d).

Although it appears that Depth First Iterative Deepening (DFID) search wastes a great deal of time in the iterations prior to the one that finds a solution, this extra work is usually insiginificant. To see this, note that the number of nodes at depth d is b^2, and each of these nodes are generated once, during the final iteration. The number of nodes at depth d-1 is b^d-1, and each of these are generated twice, once during the final iteration, and once during the penultimate iteration. In general, the number of nodes generated by DFID is b^d + 2b^d-1 + 3b^d-2 +… + db. This is asymptomatically O(b^d) if b is greater than one, since for larger values of d the lower order terms become insignificant. In other words, most of the work goes into the final iteration, and the cost of the previous iterations is relatively small. The ratio of the number of nodes generated by DFID to those generated by breadth-first search on a once is approximately b/(b-1). In fact, depth first iterative deepening is asymptomatically optimal in terms of time and space among all brute-force shortest path algorithms on a tree.

If the edge costs differ from one another then one can run an iterative deepening version of uniform-cost search, where the depth cutoff is replaced by a cutoff on the g(n) cost of a node. At the end of each iteration, the threshold for the next iteration is set to the minimum cost of all nodes generated on the previous iteration whose cost exceeded the previous threshold.

In a graph with cycles, however, breadth-first search may be much more effficient than any depth-first search. The reason is that a breadth-first search can check for duplicate nodes whereas a depth-first search cannot. Thus, the complexity of breadth-first search grows only as the number of nodes at a given depth, while the complexity of depth-first search depends on the number of paths of a given length. For example, in a square grid, the number of nodes within a radious r of the origin is O(r^2), whereas the number of paths of length r is O(3^r), since there are three childrenof every node, counting its parent. Thus, in a graph with a large number of very short cycles, breadth-first search is preferable to depth-first search, if sufficient memory is available.

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Brute Force Search

Depth First Search

Bidirectional Search

Breadth First Search (BFS) searches breadth-wise in the problem space. Breadth-First search is like traversing a tree where each node is a state which may a be a potential candidate for solution. It expands nodes from the root of the tree and then generates one level of the tree at a time until a solution is found. It is very easily implemented by maintaining a queue of nodes. Initially the queue contains just the root. In each iteration, node at the head of the queue is removed and then expanded. The generated child nodes are then added to the tail of the queue.

1. Create a variable called NODE-LIST and set it to the initial state.
2. Loop until the goal state is found or NODE-LIST is empty.
1. Remove the first element, say E, from the NODE-LIST. If NODE-LIST was empty then quit.
2. For each way that each rule can match the state described in E do:

i) Apply the rule to generate a new state.
ii) If the new state is the goal state, quit and return this state.
iii) Otherwise add this state to the end of NODE-LIST

Since it never generates a node in the tree until all the nodes at shallower levels have been generated, breadth-first search always finds a shortest path to a goal. Since each node can be generated in constant time, the amount of time used by Breadth first search is proportional to the number of nodes generated, which is a function of the branching factor b and the solution d. Since the number of nodes at level d is bd, the total number of nodes generated in the worst case is b + b2 + b3 +… + bd i.e. O(bd) , the asymptotic time complexity of breadth first search.

Look at the above tree with nodes starting from root node, R at the first level, A and B at the second level and C, D, E and F at the third level. If we want to search for node E then BFS will search level by level. First it will check if E exists at the root. Then it will check nodes at the second level. Finally it will find E a the third level.

1. Breadth first search will never get trapped exploring the useless path forever.
2. If there is a solution, BFS will definitely find it out.
3. If there is more than one solution then BFS can find the minimal one that requires less number of steps.

1. The main drawback of Breadth first search is its memory requirement. Since each level of the tree must be saved in order to generate the next level, and the amount of memory is proportional to the number of nodes stored, the space complexity of BFS is O(bd). As a result, BFS is severely space-bound in practice so will exhaust the memory available on typical computers in a matter of minutes.
2. If the solution is farther away from the root, breath first search will consume lot of time.

#### For further study

External-Memory Breadth-First Search with Sublinear I/O

# Generate-And-Test Algorithm

Generate-and-test search algorithm is a very simple algorithm that guarantees to find a solution if done systematically and there exists a solution.

# Algorithm: Generate-And-Test

1.Generate a possible solution.
2.Test to see if this is the expected solution.
3.If the solution has been found quit else go to step 1.

Potential solutions that need to be generated vary depending on the kinds of problems. For some problems the possible solutions may be particular points in the problem space and for some problems, paths from the start state.

Figure: Generate And Test

Generate-and-test, like depth-first search, requires that complete solutions be generated for testing. In its most systematic form, it is only an exhaustive search of the problem space. Solutions can also be generated randomly but solution is not guaranteed. This approach is what is known as British Museum algorithm: finding an object in the British Museum by wandering randomly.

# Systematic Generate-And-Test

While generating complete solutions and generating random solutions are the two extremes there exists another approach that lies in between. The approach is that the search process proceeds systematically but some paths that unlikely to lead the solution are not considered. This evaluation is performed by a heuristic function.

Depth-first search tree with backtracking can be used to implement systematic generate-and-test procedure. As per this procedure, if some intermediate states are likely to appear often in the tree, it would be better to modify that procedure to traverse a graph rather than a tree.

# Generate-And-Test And Planning

Exhaustive generate-and-test is very useful for simple problems. But for complex problems even heuristic generate-and-test is not very effective technique. But this may be made effective by combining with other techniques in such a way that the space in which to search is restricted. An AI program DENDRAL, for example, uses plan-Generate-and-test technique. First, the planning process uses constraint-satisfaction techniques and creates lists of recommended and contraindicated substructures. Then the generate-and-test procedure uses the lists generated and required to explore only a limited set of structures. Constrained in this way, generate-and-test proved highly effective. A major weakness of planning is that it often produces inaccurate solutions as there is no feedback from the world. But if it is used to produce only pieces of solutions then lack of detailed accuracy becomes unimportant.

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