🧊 CubeState Explorer

A Rubik’s Cube solver built to demonstrate how real-world problems can be modeled, decomposed, and solved using core computer science principles, object-oriented design, and search algorithms.

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Overview

This project explores how a physical object like a Rubik’s Cube can be transformed into a computational model. It focuses on breaking down a complex system into manageable components and solving it using algorithmic strategies commonly used in Artificial Intelligence.

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Key Learnings

Modeling Real-World Problems

• Transformed a physical Rubik’s Cube into a structured computational representation.
• Designed abstractions that allow the cube to be manipulated programmatically.

Problem Decomposition

• Broke down the complete solver into smaller, independent components:
  • Cube representation
  • Solver logic
• Applied modular design to improve clarity and scalability.

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Object-Oriented Design

Implemented strong OOP principles throughout the project:

• Classes and Objects
  Represented cube states, moves, and solver logic.

• Inheritance
  Created reusable base classes such as:

  • Rubik’s Cube Base Class
  • Pattern Database Base Class

• Abstraction & Virtual Functions
  Defined abstract behaviors and extended them through derived classes.

• Operator Overloading
  Enabled intuitive manipulation of cube states.

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Algorithms and AI Concepts

Pathfinding Algorithms

Applied classical search algorithms to solve the cube:

• BFS (Breadth-First Search)
• DFS (Depth-First Search)
• IDDFS (Iterative Deepening DFS)
• IDA* (Iterative Deepening A*)

These algorithms demonstrate how search techniques can solve structured state-space problems.

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Heuristic Search

• Implemented A* and IDA* for optimized solving.
• Built Pattern Databases to improve heuristic accuracy.
• Used heuristics to significantly reduce search space and computation time.

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Advanced Concepts

Custom Data Structures

• Used unordered_map with custom objects.
• Designed custom hash functions for efficient state lookup.

Memory Optimization

• Leveraged bit manipulation to store cube states compactly.

File Handling

• Implemented functionality to save and load pattern databases.

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Black Box Abstraction

Introduced the concept of Black Boxing:

• Designed components (like the Permutation Indexer) where:
  • Input is provided
  • Output is expected
  • Internal implementation is abstracted away

This improves modularity and usability.

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Conclusion

This project demonstrates how:

• Complex real-world systems can be modeled in software
• Large problems can be broken into smaller, manageable parts
• Core algorithms and data structures can be applied to solve AI-related challenges
• Clean architecture and OOP principles lead to scalable and maintainable systems

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