Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Machine Learning - Special issue on inductive transfer
A Theory of Program Size Formally Identical to Information Theory
Journal of the ACM (JACM)
New error bounds for Solomonoff prediction
Journal of Computer and System Sciences
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Dynamic Programming
General Loss Bounds for Universal Sequence Prediction
General Loss Bounds for Universal Sequence Prediction
Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Defensive universal learning with experts
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distributions. We unify both theories and give strong arguments that the resulting universal AIξ model behaves optimally in any computable environment. The major drawback of the AIξ model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIξtl, which is still superior to any other time t and length l bounded agent. The computation time of AIξtl is of the order tċ2l.