39,564 research outputs found
A Hypercomputation in Brouwer's Constructivism
In contrast to other constructivist schools, for Brouwer, the notion of
"constructive object" is not restricted to be presented as `words' in some
finite alphabet of symbols, and choice sequences which are non-predetermined
and unfinished objects are legitimate constructive objects. In this way,
Brouwer's constructivism goes beyond Turing computability. Further, in 1999,
the term hypercomputation was introduced by J. Copeland. Hypercomputation
refers to models of computation which go beyond Church-Turing thesis. In this
paper, we propose a hypercomputation called persistently evolutionary Turing
machines based on Brouwer's notion of being constructive.Comment: This paper has been withdrawn by the author due to crucial errors in
theorems 4.6 and 5.2 and definition 4.
Defect Particle Kinematics in One-Dimensional Cellular Automata
Let A^Z be the Cantor space of bi-infinite sequences in a finite alphabet A,
and let sigma be the shift map on A^Z. A `cellular automaton' is a continuous,
sigma-commuting self-map Phi of A^Z, and a `Phi-invariant subshift' is a
closed, (Phi,sigma)-invariant subset X of A^Z. Suppose x is a sequence in A^Z
which is X-admissible everywhere except for some small region we call a
`defect'. It has been empirically observed that such defects persist under
iteration of Phi, and often propagate like `particles'. We characterize the
motion of these particles, and show that it falls into several regimes, ranging
from simple deterministic motion, to generalized random walks, to complex
motion emulating Turing machines or pushdown automata. One consequence is that
some questions about defect behaviour are formally undecidable.Comment: 37 pages, 9 figures, 3 table
Computation Environments, An Interactive Semantics for Turing Machines (which P is not equal to NP considering it)
To scrutinize notions of computation and time complexity, we introduce and
formally define an interactive model for computation that we call it the
\emph{computation environment}. A computation environment consists of two main
parts: i) a universal processor and ii) a computist who uses the computability
power of the universal processor to perform effective procedures. The notion of
computation finds it meaning, for the computist, through his
\underline{interaction} with the universal processor.
We are interested in those computation environments which can be considered
as alternative for the real computation environment that the human being is its
computist. These computation environments must have two properties: 1- being
physically plausible, and 2- being enough powerful.
Based on Copeland' criteria for effective procedures, we define what a
\emph{physically plausible} computation environment is.
We construct two \emph{physically plausible} and \emph{enough powerful}
computation environments: 1- the Turing computation environment, denoted by
, and 2- a persistently evolutionary computation environment, denoted by
, which persistently evolve in the course of executing the computations.
We prove that the equality of complexity classes and
in the computation environment conflicts with the
\underline{free will} of the computist.
We provide an axiomatic system for Turing computability and
prove that ignoring just one of the axiom of , it would not be
possible to derive from the rest of axioms.
We prove that the computist who lives inside the environment , can never
be confident that whether he lives in a static environment or a persistently
evolutionary one.Comment: 33 pages, interactive computation, P vs N
The Geometry of Concurrent Interaction: Handling Multiple Ports by Way of Multiple Tokens (Long Version)
We introduce a geometry of interaction model for Mazza's multiport
interaction combinators, a graph-theoretic formalism which is able to
faithfully capture concurrent computation as embodied by process algebras like
the -calculus. The introduced model is based on token machines in which
not one but multiple tokens are allowed to traverse the underlying net at the
same time. We prove soundness and adequacy of the introduced model. The former
is proved as a simulation result between the token machines one obtains along
any reduction sequence. The latter is obtained by a fine analysis of
convergence, both in nets and in token machines
A Computable Measure of Algorithmic Probability by Finite Approximations with an Application to Integer Sequences
Given the widespread use of lossless compression algorithms to approximate
algorithmic (Kolmogorov-Chaitin) complexity, and that lossless compression
algorithms fall short at characterizing patterns other than statistical ones
not different to entropy estimations, here we explore an alternative and
complementary approach. We study formal properties of a Levin-inspired measure
calculated from the output distribution of small Turing machines. We
introduce and justify finite approximations that have been used in some
applications as an alternative to lossless compression algorithms for
approximating algorithmic (Kolmogorov-Chaitin) complexity. We provide proofs of
the relevant properties of both and and compare them to Levin's
Universal Distribution. We provide error estimations of with respect to
. Finally, we present an application to integer sequences from the Online
Encyclopedia of Integer Sequences which suggests that our AP-based measures may
characterize non-statistical patterns, and we report interesting correlations
with textual, function and program description lengths of the said sequences.Comment: As accepted by the journal Complexity (Wiley/Hindawi
An unpredictability approach to finite-state randomness
AbstractThis paper investigates the concept of randomness within a complexity theoretic framework. We consider an unpredictability approach for defining randomness in which the preditions are carried out by finite-state automata. Our model of a finite-state predicting machine (FPM) reads a binary sequence from left to right and depending on the machine's current state will generate, at each point, one of three possible values: 0, 1, or #. A response of 0 or 1 is to be taken as the FPMs prediction of the next input. A # means no prediction of the next input is made. We say that an infinite binary sequence appears random to an FPM if no more than half of the predictions made of the sequence's terms by the FPM are correct. The main result of this paper is to establish the equivalence of the sequences which appear random to all FPMs and the ∞-distributed sequences, where a binary sequence is called ∞-distributed if every string of length k occurs in the sequence with frequency 2−k, for all positive integers k. We also explicitly construct machines that exhibit success in predicting the sequences which are not ∞-distributed. Finally, we show that for any given ∞-distributed sequence, all infinite subsequences which are constructible from FPMs are also ∞-distributed
Verifying Time Complexity of Deterministic Turing Machines
We show that, for all reasonable functions , we can
algorithmically verify whether a given one-tape Turing machine runs in time at
most . This is a tight bound on the order of growth for the function
because we prove that, for and , there
exists no algorithm that would verify whether a given one-tape Turing machine
runs in time at most .
We give results also for the case of multi-tape Turing machines. We show that
we can verify whether a given multi-tape Turing machine runs in time at most
iff for some .
We prove a very general undecidability result stating that, for any class of
functions that contains arbitrary large constants, we cannot
verify whether a given Turing machine runs in time for some
. In particular, we cannot verify whether a Turing machine
runs in constant, polynomial or exponential time.Comment: 18 pages, 1 figur
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