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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2103.04289/</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/2103.04289/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data — mentioned by smnscu</title><link>https://news.ycombinator.com/item?id=32716581</link><guid isPermaLink="false">urn:hackerpapers:mention:2103.04289:32716581</guid><pubDate>Sun, 04 Sep 2022 19:08:02 GMT</pubDate><description>&lt;p&gt;&amp;gt; What is the human reward function? https://en.wikipedia.org/wiki/Maslow%27s_hierarchy_of_needs Also, I don&amp;#x27;t know the right keywords to look for it, but it sounds like OP&amp;#x27;s (fascinating) question should have been tackled in scientific literature. e.g. found this in 10 seconds https://arxiv.org/abs/2103.04289&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2103.04289/"&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>
