Victory Chess Bot

Winning rate
Winning rate

Inspiration

We've always been captivated by the strategic depth of chess. The combination of logic, foresight, and creativity required to play the game at a high level is truly inspiring. Building a chess bot was our way of delving deeper into the intricacies of the game.

What it does

Our chess bot engine is designed to play chess autonomously. It uses a combination of algorithms, heuristics, and machine learning techniques to analyze positions, predict moves, and make strategic decisions. The goal is to create a bot that not only plays well but also adapts and learns from its experiences.

How we built it

The engine is built using a combination of programming languages, such as Python, and popular chess libraries. We implemented classical chess algorithms like minimax with alpha-beta pruning for decision-making. Additionally, We explored neural network architectures to enhance the bot's ability to learn and improve over time.

Challenges we ran into

We tried to use scikit learn to build our neural network, but it only support one dimensional array, which we don't think it can provide great model. So we changed our plan to implement our algorithm to use minimax

Accomplishments that we're proud of

Our winning rate is 98%

What we learned

Developing a chess bot involves diving deep into algorithmic thinking. Implementing classical algorithms like minimax and alpha-beta pruning helps us understand how to efficiently explore and evaluate the vast search space of possible moves.