A Philosophical Treatise of Universal Induction

@article{Rathmanner2011APT,
  title={A Philosophical Treatise of Universal Induction},
  author={Samuel Rathmanner and Marcus Hutter},
  journal={Entropy},
  year={2011},
  volume={13},
  pages={1076-1136}
}
  • Samuel Rathmanner, Marcus Hutter
  • Published 2011
  • Computer Science, Mathematics
  • Entropy
  • Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework… CONTINUE READING

    Paper Mentions

    A philosophical basis for hydrological uncertainty
    • 61
    One Decade of Universal Artificial Intelligence
    • 30
    • PDF
    Intelligence Explosion: Evidence and Import
    • 52
    • PDF
    Probabilities on Sentences in an Expressive Logic
    • 23
    • PDF
    Replacing Causal Faithfulness with Algorithmic Independence of Conditionals
    • 30
    • PDF
    Can Intelligence Explode?
    • 25
    • PDF
    No Free Lunch versus Occam's Razor in Supervised Learning
    • 24
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 73 REFERENCES
    A Formal Theory of Inductive Inference. Part II
    • 1,561
    • PDF
    An Introduction to Kolmogorov Complexity and Its Applications
    • 3,532
    • PDF
    Probability theory: the logic of science
    • 1,555
    • PDF
    No free lunch theorems for optimization
    • 7,185
    • PDF
    Treatise of Human Nature
    • 8,155
    • Highly Influential
    • PDF
    An essay towards solving a problem in the doctrine of chances
    • 2,078
    • PDF
    The similarity metric
    • 1,108
    • PDF
    Can We Solve the Mind–Body Problem?
    • 466
    • PDF
    The Need for Biases in Learning Generalizations
    • 364
    • PDF