  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 parameterless theory of universal Artificial Intelligence. We give strong
arguments that the resulting AIXI model is the most intelligent unbiased agent
possible. We outline for a number of problem classes, including sequence
prediction, strategic games, function minimization, reinforcement and
supervised learning, how the AIXI model can formally solve them. The major
drawback of the AIXI model is that it is uncomputable. To overcome this
problem, we construct a modified algorithm AIXI-tl, which is still effectively
more intelligent than any other time t and space l bounded agent. The
computation time of AIXI-tl is of the order tx2^l. Other discussed topics are
formal definitions of intelligence order relations, the horizon problem and
relations of the AIXI theory to other AI approaches.
