AI Maze Navigation Agent 🚀

Unlock the future of autonomous navigation with our cutting-edge AI Maze Navigation Agent! Built to impress, this system showcases the power of AI in real-time decision-making and performance optimization.

🌟 Overview

Experience the brilliance of autonomous pathfinding with our AI Maze Navigation system. Utilizing the advanced gpt-4o-mini model, this project demonstrates unparalleled real-time decision analysis and environmental scanning, all powered by the Vercel AI SDK.

🛠️ Technical Stack

• Runtime: Bun
• Language: TypeScript 5.x
• AI Model: gpt-4o-mini by OpenAI
• AI SDK: Vercel AI SDK with OpenAI Provider
• Development Tools: Biome, Zod for validation

🤖 AI Decision Making System

🌐 Tool-based Navigation

The AI navigates the maze through three sophisticated tools:

1. Scan Tool

   • Analyzes the environment
   • Identifies current and target positions
   • Calculates Manhattan distance to the target
   • Lists valid moves and tracks visited positions
   • Reports on target acquisition status

2. Move Tool

   • Executes movements (UP, DOWN, LEFT, RIGHT) with explicit reasoning
   • Includes confidence scoring and alternative considerations
   • Predicts outcomes and validates moves against obstacles

3. Submit Tool

   • Finalizes the navigation attempt
   • Provides a comprehensive path analysis with confidence scoring
   • Generates detailed strategy explanations

📋 AI Prompting Strategy

The AI is guided by a focused prompting structure:

1. Core Directives

   • Efficiently navigate from start (P) to target (T)
   • Avoid walls and boundaries
   • Optimize the path

2. Strategic Guidelines

   • Continuous environment scanning
   • Evaluate moves based on distance reduction
   • Minimize backtracking and make confidence-based decisions

3. Decision Requirements

   • Provide explicit reasoning for each move
   • Consider alternative paths with confidence assessment
   • Predict outcomes accurately

📈 Performance Monitoring

🔍 Real-time Metrics

Monitor performance with real-time indicators:

1. Navigation Metrics

   • Move count and validity
   • Path optimization ratio
   • Backtracking frequency
   • Exploration efficiency

2. Decision Quality

   • Move confidence accuracy
   • Prediction vs. actual outcomes
   • Depth of alternative considerations
   • Quality of decision reasoning

3. Path Analysis

   • Optimal path deviation
   • Target approach efficiency
   • Space coverage
   • Movement pattern analysis

🌟 Visual Feedback

Get instant visual insights with:

• Current maze state
• Player position and movement history
• Decision confidence levels
• Performance metrics
• AI reasoning for each move

💻 Project Structure

src/
├── tools.ts      # AI interaction tools
├── gameLogic.ts  # Core mechanics
├── gameState.ts  # State management
├── telemetry.ts  # Performance tracking
└── display.ts    # Visualization

🚀 Quick Start

bun install
echo "OPENAI_API_KEY=your_key_here" > .env
bun run start

📜 License

MIT