Runescape Combat Bot - Charlie Cullen

Educational Project Disclaimer

This project was created purely for educational purposes to explore automation, pattern recognition, and bot detection systems. It demonstrates technical concepts in computer vision, AI decision-making, and anti-detection techniques. The project is not intended for actual use in live game environments.

Project Overview

An educational automation tool demonstrating advanced pattern recognition, computer vision techniques, and intelligent decision-making algorithms. The project explores bot detection evasion strategies, randomized behavior patterns, and adaptive AI systems in the context of game automation as a learning exercise in software engineering and AI.

Key Features

Computer vision for game state detection and object recognition
Randomized timing and human-like behavior patterns
Adaptive AI decision-making for combat scenarios
Anti-detection mechanisms and behavior variation
Screen capture and color-based pattern matching
Configurable behavior profiles and strategy selection

Technical Implementation

Architecture

The system uses a modular architecture separating perception, decision-making, and action:

Vision Module: Screen capture and pattern recognition using color matching
Decision Engine: State machine-based AI for combat strategy
Action System: Mouse and keyboard automation with randomization
Anti-Detection: Human-like timing variations and behavior patterns
Configuration Manager: Behavior profile customization

Technologies Used

Java: Core programming language
Robot Class: Screen capture and input simulation
BufferedImage: Image processing and color detection
State Machines: AI behavior management
Random Distributions: Human-like timing generation

Challenges & Solutions

Challenge: Reliable Pattern Recognition

Solution: Implemented tolerance-based color matching with multiple validation points to handle different lighting conditions and visual variations.

Challenge: Human-like Behavior

Solution: Used Gaussian distributions for timing variations and implemented Bezier curves for natural mouse movement paths.

Challenge: State Management

Solution: Developed a robust state machine with error recovery to handle unexpected game states and maintain consistent operation.

What I Learned

Computer vision fundamentals and pattern recognition techniques
Human behavior modeling and randomization strategies
State machine design for complex AI systems
Screen capture and input automation APIs
Detection evasion techniques and their countermeasures
Ethical considerations in automation development

Project Links

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