• Corpus ID: 17607306

Solomonoff Induction

@inproceedings{Legg1997SolomonoffI,
  title={Solomonoff Induction},
  author={Shane Legg},
  year={1997}
}
  • S. Legg
  • Published 1997
  • Computer Science
Solomonoff's induction method is an interesting theoretical model of what could be considered a perfect inductive inference system. Furthermore, it can be proven that a whole range of commonly used induction principles are computable approximations or special cases of Solomonoff's method. As such, Solomonoff induction provides us with a powerful and unifying perspective on the many diverse principles and methods that exist to deal with induction problems. The foundations of Solomonoff induction… 
4 Citations
Universal Semimeasures: An Introduction
  • N. Hay
  • Computer Science, Mathematics
  • 2007
TLDR
This work demonstrates the existence of universal semimeasure using a novel proof of the equivalence between enumerable semimeasures and processes, and defines a novel complexity measure, simulation complexity, which generalises monotone complexity.
The Relativity of Induction
TLDR
The core problem of generalization is explored and it is shown that long-accepted Occam's razor and parsimony principles are insufficient to ground learning, and a set of relativistic principles are derived that yield clearer insight into the nature and dynamics of learning.
Formalizing preference utilitarianism in physical world models
TLDR
This paper uses Bayesian inference to introduce a formalization of preference utilitarianism in physical world models, specifically cellular automata.
Investigations of galaxy clusters using gravitational lensing
In this dissertation, we discuss the properties of galaxy clusters that have been determined using strong and weak gravitational lensing. A galaxy cluster is a collection of galaxies that are bound

References

SHOWING 1-10 OF 10 REFERENCES
Inductive Reasoning and Kolmogorov Complexity
TLDR
The thesis is developed that Solomonoff's method is fundamental in the sense that many other induction principles can be viewed as particular ways to obtain computable approximations to it.
Information and Randomness: An Algorithmic Perspective
An Introduction to Kolmogorov Complexity and Its Applications
TLDR
The book presents a thorough treatment of the central ideas and their applications of Kolmogorov complexity with a wide range of illustrative applications, and will be ideal for advanced undergraduate students, graduate students, and researchers in computer science, mathematics, cognitive sciences, philosophy, artificial intelligence, statistics, and physics.
Complexity-based induction systems: Comparisons and convergence theorems
TLDR
Levin has shown that if tilde{P}'_{M}(x) is an unnormalized form of this measure, and P( x) is any computable probability measure on strings, x, then \tilde{M}'_M}\geqCP (x) where C is a constant independent of x .
THE COMPLEXITY OF FINITE OBJECTS AND THE DEVELOPMENT OF THE CONCEPTS OF INFORMATION AND RANDOMNESS BY MEANS OF THE THEORY OF ALGORITHMS
TLDR
The present article is a survey of the fundamental results connected with the concept of complexity as the minimum number of binary signs containing all the information about a given object that are sufficient for its recovery (decoding).
Solomonoff . Complexitybased induction systems : comparisons and convergence theorems
  • IEEE Trans . IT -
  • 1978
Solomonoff . A formal theory of inductive inference , Part 1 and Part 2
  • Inform . and Control
  • 1964
A formal theory of inductive inference, Part 1 and Part 2
  • Inform. and Control
  • 1964
A formal theory of inductive inference, Part 1 and Part 2, Inform. and Control
  • 1964
A formal theory of inductive inference, Part 1 and Part 2
  • Inform. and Control
  • 1964