An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
New error bounds for Solomonoff prediction
Journal of Computer and System Sciences
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
A Computer Scientist's View of Life, the Universe, and Everything
Foundations of Computer Science: Potential - Theory - Cognition, to Wilfried Brauer on the occasion of his sixtieth birthday
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Dynamic Programming
Optimal Ordered Problem Solver
Machine Learning
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
A Monte-Carlo AIXI approximation
Journal of Artificial Intelligence Research
Extending universal intelligence models with formal notion of representation
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Universal induction with varying sets of combinators
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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Universal induction solves in principle the problem of choosing a prior to achieve optimal inductive inference. The AIXI theory, which combines control theory and universal induction, solves in principle the problem of optimal behavior of an intelligent agent. A practically most important and very challenging problem is to find a computationally efficient (if not optimal) approximation for the optimal but incomputable AIXI theory. We propose such an approximation based on a Monte Carlo algorithm that samples programs according to their algorithmic probability. The approach is specifically designed to deal with real world problems (the agent processes observed data and makes plans over range of divergent time scales) under limited computational resources.