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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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GrooveSquid.com Paper Summaries

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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.

Keywords

» Artificial intelligence