Designing a neural network for a game like Draughts involves several key decisions, and there’s no single “correct” approach. However, here are a few guiding questions that can help structure the design:

1. What should the network predict? Since Draughts is a turn-based game with many possible moves, the neural network should suggest the best move based on the current board position. This can be done in two ways:

Classification: The network picks the best move from a list of possible moves. Regression: The network assigns a score to each move, helping to choose the most effective one.

2. What inputs should the network take?

The neural network needs information about the game to decide the best move. To do this, we need to convert the board into numbers so the network can process it. Each square on the board can be represented as:

Empty (no piece) Regular piece (belongs to a player) King piece (a stronger piece that moves differently) Other helpful details include:

Whose turn it is How many pieces have been captured This way, the network gets a complete picture of the game and can make better decisions.

3. Should it be a feed-forward or recurrent network? You can use two main types of Neural Networks First one is Feed-Forward Neural Network: Feed-Forward Neural Network looks at the current board and picks the best move without considering what happened before. It treats each move as a fresh start. This works well if just looking at the board is enough to decide the best move

Another one is Recurrent Neural Network (RNN) or LSTM: Recurrent Neural Network (RNN) or LSTM remembers past moves. This is helpful if knowing previous moves changes what the best next move should be. For example, if a player is trying to avoid repeating a position or set up a long-term strategy, an RNN or LSTM can track those patterns.

So, if the game doesn’t require memory of past moves, a feed-forward network is simpler and easier to train. If remembering past moves is important, an RNN or LSTM is better.

4. Do you need hidden layers? Since Draughts is a complex game, you’ll likely need at least one hidden layer in your neural network. The number of layers and neurons depends on how detailed your board representation is and how complex you want the decision-making to be. A good approach is to start with a simple setup and then adjust based on how well it performs.

5. How will the network be trained? To train the neural network, you’ll need a dataset containing different game states and the best possible moves for each. There are two main ways to train it:

Supervised learning: The AI learns from past game data, studying moves made by skilled players. Reinforcement learning: The AI plays against itself, improving over time by learning from wins and losses. Many advanced game-playing AIs, like those used for Chess and Go, rely on deep reinforcement learning with techniques such as Deep Q-Networks (DQN) or methods inspired by AlphaZero to continuously refine their strategies.

Would love to hear your thoughts! Have you considered using reinforcement learning, or are you looking for a simpler approach?