Slides in PDF - of Marcus Hutter

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Marcus Hutter - 62 - Universal Induction & Intelligence Convergence and Loss Bounds • Total (loss) bounds: ∑ ∞ n=1 E[h n] × < K(µ) ln 2, where h t (ω
Marcus Hutter - 63 - Universal Induction & Intelligence More Stuff / Critique / Problems • Prior knowledge y can be incorporated by using “subjective” prior w U ν|y = 2−K(ν|y) or by prefixing observation x by y. • Additive/multiplicative constant fudges and U-dependence is often (but not always) harmless. • Incomputability: K and M can serve as “gold standards” which practitioners should aim at, but have to be (crudely) approximated in practice (MDL [Ris89], MML [Wal05], LZW [LZ76], CTW [WSTT95], NCD [CV05]). • The Minimum Description Length Principle: M(x) ≈ 2 −K U (x) ≈ 2 −K T (x) . Predict y of highest M(y|x) is approximately same as MDL: Predict y of smallest K T (xy).
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Foundations of Universal Induction
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Slides in PDF - of Marcus Hutter

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