<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Game Design on Matt Down</title><link>https://mattdown.ai/tags/game-design/</link><description>Recent content in Game Design on Matt Down</description><generator>Hugo</generator><language>en-us</language><copyright>Matt Down</copyright><lastBuildDate>Mon, 28 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://mattdown.ai/tags/game-design/index.xml" rel="self" type="application/rss+xml"/><item><title>Using an AI Agent to explore game dynamics in 1010!</title><link>https://mattdown.ai/posts/tenten/</link><pubDate>Mon, 28 Sep 2026 00:00:00 +0000</pubDate><guid>https://mattdown.ai/posts/tenten/</guid><description>&lt;h2 id="intro"&gt;Intro&lt;/h2&gt;
&lt;p&gt;For a while now I&amp;rsquo;ve been interested in &lt;strong&gt;AI accelerated design space exploration&lt;/strong&gt;. It&amp;rsquo;s a compelling approach that
allows the rapid iteration of game rules and balance. As someone who&amp;rsquo;s spent a lot of their career working on
early stage prototypes, I&amp;rsquo;ve seen firsthand how important it is to nail the core mechanics of a game.&lt;/p&gt;
&lt;p&gt;So how does that work? You first need to train an agent that is capable of playing the game in a way that it
can react to balance changes and even rule changes. Then you define some metrics based on game dynamics that I&amp;rsquo;ll
cover in the next section, before changing the game balance and rules to hit those metrics.&lt;/p&gt;</description></item></channel></rss>