Summary of Towards a Characterisation Of Monte-carlo Tree Search Performance in Different Games, by Dennis J.n.j. Soemers et al.
Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games
by Dennis J.N.J. Soemers, Guillaume Bams, Max Persoon, Marco Rietjens, Dimitar Sladić, Stefan Stefanov, Kurt Driessens, Mark H.M. Winands
First submitted to arxiv on: 13 Jun 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 A novel approach is proposed to characterize and understand which Monte-Carlo Tree Search (MCTS) variants perform well or poorly in various games. The research focuses on building an initial dataset comprising 268,386 plays from 61 agents across 1494 distinct games, aiming to develop predictive models that analyze the effectiveness of different MCTS variants. The paper presents a preliminary analysis and discusses lessons learned from the initial study, outlining future plans for improving the dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve artificial intelligence by creating a large dataset of game plays. The goal is to understand which techniques work well in different games. To achieve this, researchers have collected data on 268,386 moves made by 61 players across many types of games. They’ve started analyzing the data and building models that can predict how well certain techniques will perform. This could help create better artificial intelligence for playing games. |
