Depth-first and breadth-first search

The textbook shows algorithms for Depth-First Search (DFS) and Breadth-First Search (BFS) applied to tree structures. Now we extend the algorithms to graph structures. In our definition, BFS is a graph search algorithm that begins from a node (a “root” node) and explores all the neighboring nodes. Then for each of those nearest nodes, it explores their unexplored neighbor nodes, and so on, until all the nodes within the graph are explored. DFS is a graph search algorithm that starts from a node (a “root” node) and explores as far as possible along each branch before backtracking (going back to a node that has been explored before). For an undirected and connected graph, such a process could produce a Depth-First Search Tree, in which the starting node serves as the root of the tree. Whenever a new unvisited node is explored for the first time during the DFS search process, it is attached to the DFS tree as a child to the node from which it is being reached. Please note that our BFS and DFS algorithms are defined in a traditional way. The algorithms shown in the textbook try to find a goal node. In our algorithms, we are trying to explore all the nodes within the graph. Please use the Figure 1 and answer the questions. The figure shows an undirected graph and all the edges have equal costs (e.g., the lengths of all edges are equal). For simplicity, when we have a number of alternate nodes to explore, we will select them in an alphabetical order. The problems can be easily solved by hand. Assume node a is the starting point (the “root” node) from which the search starts. Figure 1 1) Apply the Breadth First Search (BFS) algorithm and show the output (list all the nodes by the order they are being explored) [15 points] 2) Apply the Depth First Search (DFS) algorithm and show the output (list all the nodes by the order they are being explored) [15 points] 3) Show the output from the previous question in the form of a DFS tree. A DFS tree is composed of edges from which a node is explored for the first time. [10 points] 2.2: A* Search [60 points] Note: This is a programming problem. You are required to write your own program to solve the problem, but it is free to choose any programming language you feel comfortable with. You are not required to include your source code in your submission. Please include a brief discussion on how the experiments are conducted (e.g., the data structures used, parameters you choose for the algorithms, etc), and how you get your conclusions for each of the questions. Please note that you need to describe assumptions based on which your solutions can be obtained. Consider an idealization of a problem where a robot has to navigate its way around obstacles. The goal is to find the shortest distance between two points on a plane that has convex polygonal obstacles. Figure 2 shows an example scene with eight polygonal obstacles where the robot has a d c e f b to move from the point start to the point end. Convince yourself that the shortest path from one polygon vertex to any other in the scene consists of straight-line segments joining some of the vertices of the polygon. (Note that the start and the end goal points may be considered polygons of size 0). 1) Suppose the state space consists of all possible positions (x, y) in the plane. How many states are there? How many paths are there to the goal? (There might be different answers to this question. Your answer should be based on a reasonable assumption and the assumptions should be explicitly shown in your submission.) [10 points] 2) Based on the above observation, define a good state space for the problem. How large is the state space? Why? [10 points] 3) Implement an algorithm to find the shortest path from the start node to the end node using an A* heuristic search. Use the straight-line distance to the end node as a heuristic function. Show your pseudo code (not your source code) for this algorithm. In addition, is this an admissible heuristic function? Why or why not? [15 points] Hint: Define the necessary functions to implement the search problem. This should include a function that takes a vertex as input and returns the set of vertices that can be reached in a straight line from the given vertex. You may need to implement a function that detects whether or not two line segments intersect. The problem can be easily solved using shortest path algorithms but you are required to use A*. 4) Explicitly present the solution (a path from the start point to the end point) for the following problem using the A* algorithm you implemented. [15 points] Figure 2 600 100 250 400 700 5 8 4 1 6 7 3 2 500 Note: This figure is for illustration purpose only. Positions of the polygons inside the figure may not reflect their actual coordinates. Polygon 1: ((220, 616), (220, 666), (251, 670), (272, 647)) Polygon 2: ((341, 655), (359, 667), (374, 651), (366, 577)) Polygon 3: ((311, 530), (311, 559), (339, 578), (361, 560), (361, 528), (336, 516)) Polygon 4: ((105, 628), (151, 670), (180, 629), (156, 577), (113, 587)) Polygon 5: ((118, 517), (245, 517), (245, 577), (118, 557)) Polygon 6: ((280, 583), (333, 583), (333, 665), (280, 665)) Polygon 7: ((252, 594), (290, 562), (264, 538)) Polygon 8: ((198, 635), (217, 574), (182, 574)) Start: (115, 655) End: (380, 560) 5) Is it possible to solve the problem using a breadth-first or a depth-first search algorithm? If the answer is yes, briefly discuss your solutions. If not, please show your arguments. [10 points]

 

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