  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 x 2^l. The discussion includes formal definitions of intelligence
order relations, the horizon problem and relations of the AIXI theory to other
AI approaches.
