elifsagban/ceng3511

Artificial Intelligence Course Projects

Artificial Intelligence Course

Group Members:

1. Yağmur Mutluer
2. Elif Nur Şağban

Project I

Breadth First Search

• Traverse through one level of children nodes, then traverse through the level of grandchildren nodes (and so on..)

Depth First Search

• Traverse through left subtree(s) first, then traverse through the right subtree(s)

Uniform Cost Search

• Searches in branches which are more or less the same in cost.

About Project I

• The code should accept the given graph from the terminal and the three algorithms should search and find the shortest path.

Resources

https://repl.it/@pn49571/shortest-distance-Breadth-first-search

https://medium.com/@akshdeep.sharma1/dfs-bfs-coding-blog-week-10-619175d9469

https://rextester.com/GDQAJ78015

Project II Solving CSPs

Kakuro Puzzle

• Figure below illustrates a simple Kakuros puzzle, solution of the puzzle, input file and the output file.
• Kakuro puzzles are similar with crosswords, but instead of letters the board is filled with digits (from 1 to 9).
• The board's squares need to be filled in with these digits in order to sum up to the specified numbers.
• To solve Kakuros Puzzle using the same digit more than once to obtain a given sum is not allowed.

Futoshiki Puzzle

• Futoshiki is a board-based puzzle game, also known under the name Unequal.It is playable on a square board having a given fixed size (4x4 for example).
• The purpose of the game is to discover the digits hidden inside the board's cells; each cell is filled with a digit between 1 and the board's size. On each row and column each digit appears exactly once; therefore, when revealed, the digits of the board form a so-called Latin square.
• At the beginning of the game some digits might be revealed. The board might also contain some inequalities between the board cells; these inequalities must be respected and can be used as clues in order to discover the remaining hidden digits.
• Each puzzle is guaranteed to have a solution and only one. In order to indicate a move, click the desired square and select a digit or the delete sign (X); you can also use the digits on your keyboard (in this case, the digit 0 is equivalent to the delete sign).

About Project II

• Both code files should accept the given input files as "kakuro_input" and "futoshiki_input.txt".
• After running both codes should write outputs into the files as "kakuro_output" and "futoshiki_output.txt"

Resources

https://developers.google.com/optimization/cp/original_cp_solver https://www.kakuros.com/ https://www.futoshiki.org/

About Project III

• This is about knapsack problem and solved by genetic algorithm.
• There are 3 files called, "w, c, v" that stores weight, capacity and value.
• Run the program such as "python knapsack.gy" and choose options as you like then output will be stored in out.txt

Project IV Mobile Price Classification using KNN

About Project IV

• This project about implementing K nearest neighbours using python without any machine learning libraries.
• The data set has 21 columns, the last one is class.
• After running the code expected outputs are knn values with accuracy rates and the Plot graph of that values.

Resources

• https://github.com/Asbetos/KNN/blob/master/knn.py
• https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/
• https://www.analyticsvidhya.com/blog/2018/08/k-nearest-neighbor-introduction-regression-python/

Project V K-means in Action 

About Project V

• This project about k-means algorithm implementation without machine learning libraries.
• The data set has two columns, "income" and "spend"
• Expected output: