Universal Algorithmic Intelligence: A Mathematical Top→Down Approach

Marcus Hutter*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    47 Citations (Scopus)


    Sequential decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameter-free theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline how the AIXI model can formally solve a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXItl that is still effectively more intelligent than any other time t and length l bounded agent. The computation time of AIXItl is of the order t·2l. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations of the AIXI theory to other AI approaches.

    Original languageEnglish
    Title of host publicationArtificial General Intelligence
    EditorsBen Goertzel, Pennachin Pennachin
    Number of pages64
    Publication statusPublished - 2007

    Publication series

    NameCognitive Technologies
    ISSN (Print)1611-2482


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