Alpha2048 - How I Taught My Computer to Play 1024

Overview

Discover how to teach a computer to play 1024 using AI techniques, exploring machine learning concepts and implementation strategies in JavaScript.

Syllabus

• Introduction to AI and Game Automation
Overview of AI in Games
Introduction to the 1024 Game: Rules and Objectives
Setting Goals for AI Automation in 1024
• Fundamentals of Machine Learning
Basic Concepts of Machine Learning
Supervised vs. Unsupervised Learning
Reinforcement Learning Basics
• JavaScript for AI Development
Introduction to JavaScript and Its Capabilities
Setting Up the Development Environment
JavaScript Libraries for Machine Learning
• Designing the 1024 Game Environment
Understanding the 1024 Game Mechanics
Creating a Digital Representation of the Game in JavaScript
Testing and Debugging the Game Environment
• Developing the AI Algorithm
Selecting the Appropriate AI Approach for 1024
Implementing Heuristic-Based Strategies
Introduction to the Minimax Algorithm and its Adaptations
• Reinforcement Learning for 1024
Fundamentals of Reinforcement Learning
Implementing Q-Learning for Game Strategies
Training the AI Model
• Evaluating and Optimizing AI Performance
Metrics for Evaluating AI Performance in 1024
Debugging and Refining Algorithm Efficiency
Exploring Advanced Optimization Techniques
• Deploying and Testing the 1024 AI
Integration of AI with the Game Interface
Running Simulations and Performance Testing
User Interaction and Feedback Mechanisms
• Future Directions and Advanced Concepts
Extensions to More Complex Games
Exploring Neural Networks and Deep Learning
Ethics and Limitations in Game AI Development
• Conclusion and Course Wrap-Up
Recap of Key Concepts and Techniques
Final Project: Showcasing Your AI's Performance
Resources for Further Learning and Exploration

Subjects

Conference Talks