Learning factorial codes by predictability minimization
Neural Computation
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
The complexity of loop programs
ACM '67 Proceedings of the 1967 22nd national conference
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Adversarial Sequence Prediction
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
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Under Legg's and Hutter's formal measure [1], performance in easy environments counts more toward an agent's intelligence than does performance in difficult environments. An alternate measure of intelligence is proposed based on a hierarchy of sets of increasingly difficult environments, in a reinforcement learning framework. An agent's intelligence is measured as the ordinal of the most difficult set of environments it can pass. This measure is defined in both Turing machine and finite state machine models of computing. In the finite model the measure includes the number of time steps required to pass the test.