1,614 research outputs found
Natural Halting Probabilities, Partial Randomness, and Zeta Functions
We introduce the zeta number, natural halting probability and natural
complexity of a Turing machine and we relate them to Chaitin's Omega number,
halting probability, and program-size complexity. A classification of Turing
machines according to their zeta numbers is proposed: divergent, convergent and
tuatara. We prove the existence of universal convergent and tuatara machines.
Various results on (algorithmic) randomness and partial randomness are proved.
For example, we show that the zeta number of a universal tuatara machine is
c.e. and random. A new type of partial randomness, asymptotic randomness, is
introduced. Finally we show that in contrast to classical (algorithmic)
randomness--which cannot be naturally characterised in terms of plain
complexity--asymptotic randomness admits such a characterisation.Comment: Accepted for publication in Information and Computin
Computing A Glimpse of Randomness
A Chaitin Omega number is the halting probability of a universal Chaitin
(self-delimiting Turing) machine. Every Omega number is both computably
enumerable (the limit of a computable, increasing, converging sequence of
rationals) and random (its binary expansion is an algorithmic random sequence).
In particular, every Omega number is strongly non-computable. The aim of this
paper is to describe a procedure, which combines Java programming and
mathematical proofs, for computing the exact values of the first 64 bits of a
Chaitin Omega:
0000001000000100000110001000011010001111110010111011101000010000. Full
description of programs and proofs will be given elsewhere.Comment: 16 pages; Experimental Mathematics (accepted
Numerical Evaluation of Algorithmic Complexity for Short Strings: A Glance into the Innermost Structure of Randomness
We describe an alternative method (to compression) that combines several
theoretical and experimental results to numerically approximate the algorithmic
(Kolmogorov-Chaitin) complexity of all bit strings up to 8
bits long, and for some between 9 and 16 bits long. This is done by an
exhaustive execution of all deterministic 2-symbol Turing machines with up to 4
states for which the halting times are known thanks to the Busy Beaver problem,
that is 11019960576 machines. An output frequency distribution is then
computed, from which the algorithmic probability is calculated and the
algorithmic complexity evaluated by way of the (Levin-Zvonkin-Chaitin) coding
theorem.Comment: 29 pages, 5 figures. Version as accepted by the journal Applied
Mathematics and Computatio
Most Programs Stop Quickly or Never Halt
Since many real-world problems arising in the fields of compiler
optimisation, automated software engineering, formal proof systems, and so
forth are equivalent to the Halting Problem--the most notorious undecidable
problem--there is a growing interest, not only academically, in understanding
the problem better and in providing alternative solutions. Halting computations
can be recognised by simply running them; the main difficulty is to detect
non-halting programs. Our approach is to have the probability space extend over
both space and time and to consider the probability that a random -bit
program has halted by a random time. We postulate an a priori computable
probability distribution on all possible runtimes and we prove that given an
integer k>0, we can effectively compute a time bound T such that the
probability that an N-bit program will eventually halt given that it has not
halted by T is smaller than 2^{-k}. We also show that the set of halting
programs (which is computably enumerable, but not computable) can be written as
a disjoint union of a computable set and a set of effectively vanishing
probability. Finally, we show that ``long'' runtimes are effectively rare. More
formally, the set of times at which an N-bit program can stop after the time
2^{N+constant} has effectively zero density.Comment: Shortened abstract and changed format of references to match Adv.
Appl. Math guideline
Ultimate Intelligence Part I: Physical Completeness and Objectivity of Induction
We propose that Solomonoff induction is complete in the physical sense via
several strong physical arguments. We also argue that Solomonoff induction is
fully applicable to quantum mechanics. We show how to choose an objective
reference machine for universal induction by defining a physical message
complexity and physical message probability, and argue that this choice
dissolves some well-known objections to universal induction. We also introduce
many more variants of physical message complexity based on energy and action,
and discuss the ramifications of our proposals.Comment: Under review at AGI-2015 conference. An early draft was submitted to
ALT-2014. This paper is now being split into two papers, one philosophical,
and one more technical. We intend that all installments of the paper series
will be on the arxi
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