CS480 - Programming Assignment - IT Assignment Help

Assignment Task

Objectives:
Implement and evaluate two informed search algorithms.

Input data files:
You are provided two CSV (comma separated values) files (see Programming Assignment #01 folder in Blackboard):
driving.csv - with driving distances between state capitals.
straightline.csv - with straight line distances between state capitals.

You CANNOT modify nor rename input data files. Rows and columns in those files represent individual state data (state labels/names are in the first row and column). Numerical data in both files is either:
a non-negative integer corresponding to the distance between two state capitals,
negative integer -1 indicating that there is no direct “road” (no edge on the graph below) between two state capitals.

Problem description:
Each node represents a single state (or the District of Columbia (DC)). If two states are neighbors, there is an edge between them.

Assume that edge weights represent driving distances between state capitals.
Your task is to implement in Python two informed search algorithms:
Greedy Best First Search algorithm, and A* algorithm, and apply them to find a path between two state capitals using provided data.
Your program should:
Accept two (2) command line arguments corresponding to two states / state capitals (initial and goal states) so your code could be executed with
python cs480_P01_AXXXXXXXX.py INITIAL GOAL where:
cs480_P01_AXXXXXXXX.py is your python code file name, INITIAL is the label/name of the initial state, GOAL is the label/name of the initial state.
Example:
python cs480_P01_A11111111.py WA TX
If the number of arguments provided is NOT two (none, one, or more than two), your program should display the following error message:
ERROR: Not enough or too many input arguments.
and exit.

Load and process both input data files provided (assume that input data files are ALWAYS in the same folder as your code - this is REQUIRED!). Make sure your program is flexible enough to accommodate different input data sets (with a different graph of states and distances). Your submission will be tested using a different set of files!
Run Greedy Best First Search and A* algorithms searches to find a path between INITIAL and GOAL states and measure execution time (in seconds) for both methods.
Report results on screen in the following format:
Last Name, First Name, AXXXXXXXX solution:
Initial state: INITIAL
Goal state: GOAL

Greedy Best First Search:
Solution path: STATE1, STATE2, STATE3, …, STATEN-1, STATEN
Number of states on a path: X1
Path cost: Y1
Execution time: T1 seconds
A* Search:
Solution path: STATE1, STATE2, STATE3, …, STATEN-1, STATEN
Number of states on a path: X2
Path cost: Y2
Execution time: T2 seconds
where:
AXXXXXXXX is your IIT A number,
INITIAL is the label/name of the initial state,
GOAL is the label/name of the initial state,
STATE1, STATE2, STATE3, …, STATEN-1, STATEN is a solution represented as a list of visited states (including INITIAL and GOAL states), for example: IL, IA, NE,
If no path is found replace appropriate information with:
Solution path: FAILURE: NO PATH FOUND
Number of states on a path: 0
Path cost: 0
Execution time: T3 seconds
Now, recall the INITIAL / GOAL state pair that you used in Written Assignment #01 Problem 2 and run Greedy Best First and A* algorithms to find the path between them. Repeat this search ten (10) times and report your findings in the Table A below. If your Hill Climbing example did not reach the goal state write FAILURE.
TABLE A: Results comparison
Algorithm Visited states Number of visited states Path cost Average search time in seconds
Hill Climbing N/A
Greedy Best First Search A* What are your conclusions? Which algorithm performed better? Was the optimal path found? Write a summary below
Conclusions

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