Summary of Set-based Retrograde Analysis: Precomputing the Solution to 24-card Bridge Double Dummy Deals, by Isaac Stone et al.
Set-Based Retrograde Analysis: Precomputing the Solution to 24-card Bridge Double Dummy Deals
by Isaac Stone, Nathan R. Sturtevant, Jonathan Schaeffer
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach in game-playing programs is introduced, called setrograde analysis. This method solves states at the end of a game by working backwards towards the start, but instead of processing individual states, it operates on sets of states with the same value. The algorithm is demonstrated by computing exact solutions for Bridge double dummy card-play, achieving significant improvements over traditional retrograde algorithms. For example, in deals with 24 cards remaining to be played (which can be reduced to 10^15 states using preexisting techniques), setrograde analysis strongly solves all deals with a factor of 10^3 fewer search operations and a database with a factor of 10^4 fewer entries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Game-playing programs are getting smarter! Researchers have developed a new way to solve games by working backwards, called setrograde analysis. This method looks at sets of game states instead of individual ones. It’s super efficient and can solve really tricky card games like Bridge. Imagine playing cards with your friends – you’d want the computer to be able to solve all the possible moves in advance! That’s what this new algorithm does. |
