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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Models of Individual Behavior in Chess · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2008.10086/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2008.10086/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Models of Individual Behavior in Chess — mentioned by reidmcy</title><link>https://news.ycombinator.com/item?id=25268658</link><guid isPermaLink="false">urn:hackerpapers:mention:2008.10086:25268658</guid><pubDate>Tue, 01 Dec 2020 19:12:19 GMT</pubDate><description>&lt;p&gt;Hi everyone, We&amp;#x27;re happy to announce a research project that has been in the works for almost two years! Please meet Maia, a human-like neural network chess engine. Maia is a Leela-style framework that learns from human play instead of self-play, with the goal of making human-like moves instead of optimal moves. Maia predicts the exact moves humans play in real online games over 50% of the time. We intend Maia to power data-driven learning tools and teaching aids, as well as be a fun sparring partner to play against. We trained 9 different versions on 12M Lichess games each, one for each…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2008.10086/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
