CREATING A 2x2 CUBE SOLVER IN JAVA | PART 1/2: THE A* ALGORITHM |

Welcome to the code tutorial for solving the 2x2 Rubik's Cube! (The baby cube) If you're a coding enthusiast or simply curious about algorithmic problem-solving, you've come to the right place. In this guide, we'll explore the world of solving Rubik's Cube using code. From understanding the underlying principles of the cube to implementing efficient algorithms, we'll walk you through the step-by-step process of creating a 2x2 Rubik's Cube solver program. Whether you're a beginner or an experienced programmer, this tutorial will equip you with the knowledge and tools to tackle this intriguing puzzle programmatically. Let's dive into the exciting world of coding and conquer the Rubik's Cube together! 

Side note-1: I am just trash at programming so I started off with a 2x2 solver, but hey, the 3x3 solver will be coming soon.

Firstly, let's learn how Rubik's cube notation works: 

Side Note-2: For double notations (half turns) like R2, L2, etc. we will be using R R and L L respectively and so on.

Understanding the A* Algorithm: The A* algorithm is a popular search algorithm widely used in pathfinding and puzzle-solving scenarios. It intelligently explores possible paths to find the most optimal solution. The algorithm utilizes a heuristic evaluation function, which estimates the cost from the current state to the goal state. By combining the cost function with a priority queue, A* ensures efficient exploration of the search space.

Applying the A* Algorithm to the 2x2 Rubik's Cube: The 2x2 Rubik's Cube, also known as the Pocket Cube, has eight corner pieces that can be rearranged in various ways. Each corner can be rotated 90 degrees clockwise or counterclockwise. Although simpler than its larger counterpart, the Pocket Cube still presents a non-trivial challenge.

To apply the A* algorithm to solve the 2x2 Rubik's Cube, we need to define the state space, the goal state, and the heuristic function. In this case, the state space represents the different possible configurations of the cube, and the goal state is simply the solved configuration.

Heuristic Function: The heuristic function is crucial for guiding the A* algorithm towards the goal state efficiently. For the 2x2 Rubik's Cube, an effective heuristic function could be the count of misplaced corner pieces. This heuristic estimates the number of moves required to reach the goal state. While this heuristic is not admissible (it may overestimate the actual cost), it still provides a good approximation and allows for efficient exploration of the search space.

A* Algorithm Steps for the 2x2 Rubik's Cube: 

1. Define the initial state and the goal state.
2. Create an open set to store states to be explored and a closed set to store visited states.
3. Set the initial state as the starting point and initialize the open set.
4. While the open set is not empty: Select the state with the lowest estimated cost from the open set.
5. If the selected state is the goal state, the solution is found.
6. Generate all possible successor states from the selected state.
7. For each successor state: Calculate the estimated cost using the heuristic function.
8. If the state is not in the open or closed set, add it to the open set.
9. If the open set is empty and no solution is found, the cube is unsolvable.

While we have focused on applying the A* algorithm to the 2x2 Rubik's Cube in this blog post, it's worth noting that the algorithm can also be adapted for larger Rubik's Cubes and other similar puzzles. With the right state representation and appropriate heuristic function, the A* algorithm offers an elegant solution to a wide range of challenging puzzles.

In the next part we will dive headfirst into the astonishing world of Java as we unravel the mysteries of a 2x2 cube and the A* algorithm.

Side note-3: This blog post is a little technical just to establish my credibility as a blogger since my previous posts were as authentic and genuine as a politicians promise.