Slides in PDF - of Marcus Hutter
Slides in PDF - of Marcus Hutter
Slides in PDF - of Marcus Hutter
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<strong>Marcus</strong> <strong>Hutter</strong> - 80 - Universal Induction & Intelligence<br />
Particularly Interest<strong>in</strong>g Environments<br />
• Sequence Prediction, e.g. weather<br />
√<br />
or stock-market prediction.<br />
Strong result: Vµ ∗ − Vµ pξ = O( ), m =horizon.<br />
K(µ)<br />
m<br />
• Strategic Games: Learn to play well (m<strong>in</strong>imax) strategic zero-sum<br />
games (like chess) or even exploit limited capabilities <strong>of</strong> opponent.<br />
• Optimization: F<strong>in</strong>d (approximate) m<strong>in</strong>imum <strong>of</strong> function with as few<br />
function calls as possible. Difficult exploration versus exploitation<br />
problem.<br />
• Supervised learn<strong>in</strong>g: Learn functions by present<strong>in</strong>g (z, f(z)) pairs<br />
and ask for function values <strong>of</strong> z ′ by present<strong>in</strong>g (z ′ , ) pairs.<br />
Supervised learn<strong>in</strong>g is much faster than re<strong>in</strong>forcement learn<strong>in</strong>g.<br />
AIξ quickly learns to predict, play games, optimize, and learn supervised.