Rock, Paper, Scissors AI - IT & Computer Engineering Project Guide
1. Introduction
This document provides a detailed project guide for building a Rock, Paper, Scissors game with AI that predicts user choices using pattern recognition. This project helps students understand basic game design, AI concepts, and pattern recognition in user behavior.
2. Objective
To design and implement a Rock, Paper, Scissors game where the AI attempts to predict the user's next move based on their previous choices using pattern recognition or frequency analysis.
3. Requirements
Hardware Requirements:
· - PC or Laptop
· - 4GB RAM or higher
Software Requirements:
· - Python 3.x
· - Tkinter (for GUI) or command-line interface
· - IDE (e.g., PyCharm, VS Code)
4. Methodology
Step-by-step approach to implement the game:
1. 1. Implement basic Rock, Paper, Scissors game logic.
2. 2. Track user input history across multiple rounds.
3. 3. Analyze patterns or frequency of choices.
4. 4. Predict user’s next move based on history.
5. 5. Select AI move to counter predicted move.
6. 6. Display results and update scores.
5. Game Logic
Rules of the game:
- Rock beats Scissors
- Scissors beats Paper
- Paper beats Rock
The winner is determined each round based on the user and AI's move.
6. AI Prediction Strategy
Simple pattern recognition technique:
- Store the frequency of each user move (rock, paper, scissors)
- Predict that the user will play their most frequent move
- Counter it with the move that beats it
Example Code Snippet:
user_history = {'rock': 0, 'paper': 0, 'scissors': 0}
def predict_user_move():
most_common = max(user_history,
key=user_history.get)
counter_moves = {'rock': 'paper',
'paper': 'scissors', 'scissors': 'rock'}
return counter_moves[most_common]
7. Features
· - User-friendly interface (CLI or GUI)
· - Tracks and displays user and AI scores
· - AI learns and adapts to user behavior
· - Interactive gameplay with real-time prediction
8. Limitations
· - Limited to pattern or frequency analysis (not deep learning)
· - Predictions may not be accurate against random users
9. Future Scope
- Implement machine learning models for better prediction
- Add multiplayer support
- Visualize data on prediction accuracy and game stats
- Integrate with web or mobile platforms for broader usability
10. Conclusion
This project provides practical experience in building an interactive AI-based game. Students learn about pattern recognition, basic AI logic, and real-time decision-making. It forms a foundation for advanced AI applications.