Summary of Playing Board Games with the Predict Results Of Beam Search Algorithm, by Sergey Pastukhov
Playing Board Games with the Predict Results of Beam Search Algorithm
by Sergey Pastukhov
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A novel algorithm called PROBS (Predict Results of Beam Search) is introduced for two-player deterministic games with perfect information. Unlike existing methods relying on Monte Carlo Tree Search (MCTS), PROBS uses a simpler beam search approach. The algorithm’s performance is evaluated across various board games, showing improved winning ratios against baseline opponents. Interestingly, PROBS operates effectively even when the beam search size is smaller than the average number of turns in the game. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PROBS is an innovative way to play two-player games with perfect information. It’s different from other methods that use a lot of calculations. Our new approach uses a simpler method called beam search. We tested PROBS on different board games and found it wins more often against other players. What’s cool is that PROBS still works well even when we don’t look as far ahead. |
