Enhancing Tic-Tac-Toe with Neural Networks

Enhancing Tic-Tac-Toe with Neural Networks

Table of Contents:

1. Introduction
2. Tic-Tac-Toe with Minimax Algorithm
3. The Role of Neural Networks in Decision Making
4. Implementing Tic-Tac-Toe with Neural Networks 4.1. Spring Boot Application 4.2. Game Abstraction and User Interface 4.3. Building the Neural Network Model 4.4. Neural Network Topology 4.5. Activation Functions and Output Calculation 4.6. Back Propagation Algorithm
5. Understanding the Code Structure 5.1. Project Structure Overview 5.2. The Neural Network Class 5.3. The Neuron Class and Activation Functions 5.4. Tic-Tac-Toe Machine Learning Specification
6. File-Based Supervised Learning
7. Testing the Neural Network Agent
8. Conclusion
9. FAQs

Introduction

Tic-tac-toe is a well-known game, but I aim to enhance it by implementing a neural network as a crucial decision-making component. In the past, I utilized the Minimax algorithm for a different tic-tac-toe game, which served as a basis to prepare high-quality data for training the neural network. This article explores how neural networks can significantly improve the performance of tic-tac-toe game agents.

Tic-Tac-Toe with Minimax Algorithm

The Minimax algorithm evaluates each possible game in response to a human move and determines the most effective next move. By recursively creating a game tree, this algorithm can identify the optimal moves in a closed game like tic-tac-toe. However, the Minimax algorithm becomes inefficient in more complex games with a vast number of combinations, such as chess or go.

The Role of Neural Networks in Decision Making

To overcome the limitations of the Minimax algorithm, artificial intelligence techniques like neural networks have gained popularity. Neural networks can learn and adapt by analyzing data. By using proper data and defining specific network topologies, neural networks can provide efficient decision-making capabilities for games like tic-tac-toe.

Implementing Tic-Tac-Toe with Neural Networks

To implement tic-tac-toe with neural networks, I created a Spring Boot application. The Java language was utilized to implement the game abstraction, user interface, and neural network model. An essential part of this implementation is the neural network topology, which was determined through extensive experimentation.

Building the Neural Network Model

The input vector for the neural network consists of 18 entries representing the Current board state. Two Hidden layers, using the hyperbolic tangent activation function, provide the necessary complexity for the tic-tac-toe game. The output layer comprises nine neurons, each activated by the softmax function, which determines the move's effectiveness.

Understanding the Code Structure

The project structure includes specific packages for game-related classes and general classes for implementing the neural network. The Core class, NeuralNetwork, contains crucial methods like training, which uses backpropagation to alter weights between neurons based on prediction errors. The code implements neural networks from scratch, without relying on ready-to-use libraries.

File-based Supervised Learning

The project utilizes JSON files to store game data, which is then used as a data set for supervised learning. Two data sets, where the Minimax agent plays with 'X' and 'O' respectively, were prepared. The Tic-Tac-Toe Machine Learning Specification executes tests utilizing these data sets to train the neural network based on the stored games.

Testing the Neural Network Agent

The effectiveness of the neural network agent can be evaluated through testing. By comparing its performance against a random player over thousands of games, we can determine its accuracy. The results Show that the neural network agent achieves a success rate of approximately 90%.

Conclusion

In conclusion, implementing tic-tac-toe with neural networks enhances the decision-making capabilities of the game agents. By combining the Minimax algorithm, neural networks, and backpropagation, we can Create a powerful agent capable of making optimal moves. The showcased implementation is a proof of concept, and further improvements and updates can be expected.

FAQs

Q: What is the significance of the Minimax algorithm in tic-tac-toe? A: The Minimax algorithm evaluates possible game outcomes and determines the most effective move for the player taking it beyond simple random choices.

Q: How do neural networks improve tic-tac-toe game agents? A: Neural networks analyze high-quality data and learn patterns, enabling them to make efficient decisions and improve gameplay.

Q: Can the code provided be modified to implement neural networks in other games? A: Yes, the code structure and implementation can be adapted to suit the decision-making requirements of various games.

Q: Is the neural network agent unbeatable in tic-tac-toe? A: While the agent achieves a success rate of approximately 90%, it is not invincible. Skilled players can still find strategies to defeat it.

Q: Are there plans to further develop and update the implemented tic-tac-toe with neural networks? A: Yes, the project will be regularly updated and improved based on feedback and new advancements in artificial intelligence.