<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Negamax on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/negamax/</link><description>Recent content in Negamax on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Fri, 10 Jul 2026 08:20:25 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/negamax/index.xml" rel="self" type="application/rss+xml"/><item><title>Ultimate Tic Tac Toe</title><link>https://abaj.ai/wiki/ai/adv-search/ultimate-ttt/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/ai/adv-search/ultimate-ttt/</guid><description>&lt;p>ordinary tic-tac-toe is a solved bore — both players draw with a lookup table. glue nine boards together and add one rule about &lt;em>where&lt;/em> you are allowed to move, and suddenly the game tree is deep enough that you need actual search theory: minimax, the negamax reformulation, alpha-beta pruning, and a heuristic to stand in for the leaves you cannot reach. this page documents the agent my partner and i wrote for unsw comp3411 (assignment 3, &amp;ldquo;nine-board tic-tac-toe&amp;rdquo;), warts and all — and the warts are instructive.&lt;span class="margin-note" data-note="code: agent.py in the cs3411-9ttt repo, plus alan blair&amp;#39;s referee scaffolding servt.c / game.c and starter agents in c and java">
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