Summary of Solving a Rubik’s Cube Using Its Local Graph Structure, by Shunyu Yao et al.
Solving a Rubik’s Cube Using its Local Graph Structure
by Shunyu Yao, Mitchy Lee
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers design a new heuristic, weighted convolutional distance, for A-star search algorithm to find the solution to a scrambled Rubik’s Cube. This heuristic utilizes graph convolutional networks and attention-like weights to create a deeper search for the shortest path to the solved state. The Rubik’s Cube is represented as a graph, where states are nodes and actions are edges. The challenge lies in modeling the large state space and storing information about each state, which requires exceptional computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to solve a scrambled Rubik’s Cube using reinforcement learning. The cube is broken down into smaller pieces (nodes) connected by possible moves (edges). A computer algorithm uses this graph-like structure to search for the shortest solution. This approach can help computers find solutions more efficiently, which has applications in solving complex puzzles and problems. |
Keywords
» Artificial intelligence » Attention » Reinforcement learning
