🛠 Contribute : Found a typo? Or any other change in the description that you would like to see ? Please consider sending us a pull request in the public repo of the challenge here.
Game of thrones and endings don’t sit that well together 😛 . But what if we give you a chance to decide the conclusion to a game of kings and queens! The tryst of computers and chess is an old one, let us relive that in your new challenge. The positions of white king and rook are plotted against black King and you have to predict either the number of moves it takes for the white king to win or say if the white king loses.
Understand with code! Here is
getting started code for you.
A KRK dataset was first described in 1977. This dataset is also a KRK dataset, meaning it consists of positions of White King, White Rook, and Black King. In such a scenario if both teams play optimally(Black moves first) the only possible outcomes are either a draw or White King wins. The attributes are :
White King file (column)
White King rank (row)
White Rook file
White Rook rank
Black King file
Black King rank
optimal depth-of-win for White in 0 to 16 moves, otherwise draw(-1) .
For simplification, positions have been stored in csv file. The
7 columns, the last column is the
number of moves required to win which is
-1 in case of a draw and otherwise between
1-16 the rest
6 columns contain the position of White King and Rook and Black King.
Following files are available in the
22444samples) This csv file contains the positions of the White King, Rook and the Black king with the number of moves it takes for White king to win.
5611samples) This csv file contains the positions of the White King, Rook and the Black king but without the number of moves it takes for White king to win.
- Prepare a CSV containing header as
depthand predicted value as digit
[-1-16]denoting the number of moves it takes for White king to win.
- Name of the above file should be
- Sample submission format available at
sample_submission.csvin the resorces section.
Make your first submission here 🚀 !!
🖊 Evaluation Criteria
- 💪 Challenge Page : https://www.aicrowd.com/challenges/chess
- 🗣️ Discussion Forum : https://www.aicrowd.com/challenges/chess/discussion
- 🏆 leaderboard : https://www.aicrowd.com/challenges/chess/leaderboards
- Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
- Image Source