A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied,
with the aim of understanding the expressive power of generative models. We offer …

According to our current conception of physics, any valid physical theory is supposed to
describe the objective evolution of a unique external world. However, this condition is …

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely
proportional to their Kolmogorov complexity. There are two well-known problems. First, the …

Algorithmic information theory gives an idealized notion of compressibility that is often
presented as an objective measure of simplicity. It is suggested at times that Solomonoff …

The aim of this paper is to argue that models in cognitive science based on probabilistic
computation should not be restricted to those procedures that almost surely (with probability …

Introduction to Ray Solomonoff 85th Memorial Conference | SpringerLink Skip to main
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Solomonoff induction is held as a gold standard for learning, but it is known to be
incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior …

LNCS 7070 - Principles of Solomonoff Induction and AIXI Page 1 Principles of Solomonoff
Induction and AIXI Peter Sunehag1 and Marcus Hutter1,2 1 Research School of Computer …

How could we solve the machine learning and the artificial intelligence problem if we had
infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are …

We study the convergence of Solomonoff's universal mixture on individual Martin-Löf
random sequences. A new result is presented extending the work of Hutter and Muchnik [3] …