Assignment 5 (400 Points)

Note: Written problems must be typed and submitted as a Portable Document Format (PDF) file. All major word processors provide equation editing capabilities; therefore, all mathematics must also be typed. Here is a small list of equation editing software (there are many others):

• Apache OpenOffice Math
• Word for Mac 2011
• Office 2007
• Office 2010
• Office 2013
• MathType
• LaTeX

• Do the following Assignment:
  
  Assignment 05 is provided in the following formats:
  
  • PDF (for printing): assignment_05.pdf ... so you can work problems out by hand before typing the text and math up in a word processor.
  • Word (for editing): assignment_05.docx
  
  
  
• Download the ZIP file containing the directory structure and files for the programming problems: assignment_05.zip
  
  
• Study "A Quick Tour of the Boost Graph Library" from the Boost Graph Library (BGL) in Boost
  
  Note: The g++ version 4.7.2 and the Boost 1.53.0 headers and libraries only exist on linprog4.cs.fsu.edu.
  
  Study the following Visitor Concepts and code samples:
  • BFS Visitor Concept and bfs.cpp
  • DFS Visitor Concept and dfs.cpp
  • AStar Visitor Concept, astar_maze.cpp, and astar-cities.cpp
  
  
• After completing Assignment 05, create an assignment_05_lastname.pdf file for your written assignment and an assignment_05_lastname.zip file for your programming assignment (where lastname is your last name). Ensure that your assignment_05_lastname.zip retains the directory structure of the original zip file. In other words, ensure your zip file has the following directory structure:
  • /
    • a_star/
      • main.cpp
      • makefile
  
  Upload both your assignment_05_lastname.pdf file for your written assignment and your assignment_05_lastname.zip file for your programming assignment to the Assignment 05 location on the BlackBoard site: https://campus.fsu.edu.
  
  
• Note: Questions derived from the following sources:
  • Artificial Intelligence: A Modern Approach, Third Edition by Stuart Russell and Peter Norvig.
  • Udacity's Introduction to Artificial Intelligence course by Peter Norvig and Sebastian Thrun.
  • Coursera's Machine Learning course by Andrew Ng.
  • Coursera's Neural Networks for Machine Learning course by Geoffrey Hinton.
  • Coursera's Machine Learning course by Pedro Domingos.
  • edX's CS188.1x Artificial Intelligence course from BerkeleyX by Dan Klein.
  • Coursera's Introduction to Logic course by Michael Genesereth.
  • NPTEL's Artificial Intelligence course by Pallab Dasgupta.
  • NPTEL's Artificial Intelligence course by Anupam Basu and Sudeshna Sarkar.