From the conclusions of <a href="http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;cs&#x2F;0004001" rel="nofollow">http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;cs&#x2F;0004001</a> :<p>&quot;All tasks which require intelligence to be solved can naturally be formulated as a maximization of some expected utility in the framework of agents. We gave a functional (2) and an iterative (9) formulation of such a decision theoretic agent, which is general enough to cover all AI problem classes, as has been demonstrated by several examples. The main remaining problem is the unknown prior probability distribution AI
of the environment(s).<p>&lt;...&gt; the universal semimeasure, based on ideas from algorithmic information theory, solves the problem of the unknown prior distribution for induction problems.  No explicit learning procedure is necessary ... . We unified the theory of universal sequence prediction with the decision theoretic agent by replacing the
unknown true prior AI by an appropriately generalized universal semimeasure ξAI. We
gave strong arguments that the resulting AIξ model is the most intelligent, parameterless
and environmental&#x2F;application independent model possible.<p>The major drawback of the AIξ model is that it is
uncomputable, or more precisely, only asymptotically computable, which makes an implementation impossible. To overcome this problem, we cons tructed a modified model AI, which is still effectively more intelligent than any other time and space bounded algorithm.&quot;<p>The copy&#x2F;paste lost some information, especially symbols, so take a look at the paper for a better read.