EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 17·5 min read
Show HN: Sokoban AI Solver
TL;DR
A Sokoban AI solver has been developed that efficiently finds the optimal solution for the classic puzzle game, ensuring the fewest moves possible. This JavaScript implementation of a C++ algorithm can handle complex board configurations effectively.
✦ Why It Matters
Implement this Sokoban AI solver to enhance your own puzzle-solving applications or research.
Key Takeaways
- Explore using A* search for complex problem-solving.
- Implement compact state representations to save memory.
- Consider offline computation for highly complex puzzles.
- Utilize deadlock pruning to enhance search efficiency.
How It Works
The AI solver employs a move-optimal macro-push A* search strategy, where each search edge represents the cost of pushing a box, calculated as the keeper's shortest path to the push spot plus one move. This method allows the solver to skip individual walking steps, focusing on the overall push cost.
Additionally, it uses compact bitmask states to represent board configurations, significantly reducing memory usage and enabling the exploration of millions of states efficiently.