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About Paper On Computer Vision
This document presents SentiMATE, a novel deep learning model that uses natural language processing to learn an effective evaluation function for assessing move quality in chess. It trains a classifier on chess commentary to extract descriptions of move quality, and trains a sentiment analysis model on this commentary to predict move quality. This learned evaluation function is then used to guide decision making in a chess engine called SentiMATE. Results show SentiMATE can beat random agents and another chess engine using just a 1-move lookahead, demonstrating natural language processing can be used to sample-efficiently train a chess evaluation function.
Detailed Information
| Author: | ['Tamal Br'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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