Intelligent Agents for the Game of Go
Overview & Synopsis
Monte-Carlo Tree Search (MCTS) is a very efficient recent technology for games and planning, par- ticularly in the high-dimensional case, when the number of time steps is moderate and when there is no natural evaluation function. Surprisingly, MCTS makes very little use of learning. In this paper, we present four techniques (ontologies, Bernstein races, Contextual Monte-Carlo and poolRave) for learning agents in Monte-Carlo Tree Search, and experiment them in difficult games and in particular, the game of Go.
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Document Information
| Document Title | Intelligent Agents for the Game of Go |
|---|---|
| Author / Organization | Jean-Baptiste Hoock, Chang-Shing Lee, Arpad Rimmel, Fabien Teytaud, Olivier Teytaud, Mei-Hui Wang |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2010 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-182728 |
| Original Source Link | Visit Source Page on hal.science » |