29 research outputs found
On sparseness and Turing reducibility over the reals
AbstractWe prove some results about existence of NP-complete and NP-hard (for Turing reductions) sparse sets on different settings over the real numbers
The Computability-Theoretic Content of Emergence
In dealing with emergent phenomena, a common task is to identify useful descriptions of them in terms of the underlying atomic processes, and to extract enough computational content from these descriptions to enable predictions to be made. Generally, the underlying atomic processes are quite well understood, and (with important exceptions) captured by mathematics from which it is relatively easy to extract algorithmic con- tent. A widespread view is that the difficulty in describing transitions from algorithmic activity to the emergence associated with chaotic situations is a simple case of complexity outstripping computational resources and human ingenuity. Or, on the other hand, that phenomena transcending the standard Turing model of computation, if they exist, must necessarily lie outside the domain of classical computability theory. In this article we suggest that much of the current confusion arises from conceptual gaps and the lack of a suitably fundamental model within which to situate emergence. We examine the potential for placing emer- gent relations in a familiar context based on Turing's 1939 model for interactive computation over structures described in terms of reals. The explanatory power of this model is explored, formalising informal descrip- tions in terms of mathematical definability and invariance, and relating a range of basic scientific puzzles to results and intractable problems in computability theory
Kolmogorov complexity and the Recursion Theorem
Several classes of DNR functions are characterized in terms of Kolmogorov
complexity. In particular, a set of natural numbers A can wtt-compute a DNR
function iff there is a nontrivial recursive lower bound on the Kolmogorov
complexity of the initial segments of A. Furthermore, A can Turing compute a
DNR function iff there is a nontrivial A-recursive lower bound on the
Kolmogorov complexity of the initial segements of A. A is PA-complete, that is,
A can compute a {0,1}-valued DNR function, iff A can compute a function F such
that F(n) is a string of length n and maximal C-complexity among the strings of
length n. A solves the halting problem iff A can compute a function F such that
F(n) is a string of length n and maximal H-complexity among the strings of
length n. Further characterizations for these classes are given. The existence
of a DNR function in a Turing degree is equivalent to the failure of the
Recursion Theorem for this degree; thus the provided results characterize those
Turing degrees in terms of Kolmogorov complexity which do no longer permit the
usage of the Recursion Theorem.Comment: Full version of paper presented at STACS 2006, Lecture Notes in
Computer Science 3884 (2006), 149--16
Genericity and measure for exponential time
AbstractRecently, Lutz [14, 15] introduced a polynomial time bounded version of Lebesgue measure. He and others (see e.g. [11, 13–18, 20]) used this concept to investigate the quantitative structure of Exponential Time (E = DTIME(2lin)). Previously, Ambos-Spies et al. [2, 3] introduced polynomial time bounded genericity concepts and used them for the investigation of structural properties of NP (under appropriate assumptions) and E. Here we relate these concepts to each other. We show that, for any c ⩾ 1, the class of nc-generic sets has p-measure 1. This allows us to simplify and extend certain p-measure 1-results. To illustrate the power of generic sets we take the Small Span Theorem of Juedes and Lutz [11] as an example and prove a generalization for bounded query reductions
The Machine as Data: A Computational View of Emergence and Definability
Turing’s (Proceedings of the London Mathematical Society 42:230–265, 1936) paper on computable numbers has played its role in underpinning different perspectives on the world of information. On the one hand, it encourages a digital ontology, with a perceived flatness of computational structure comprehensively hosting causality at the physical level and beyond. On the other (the main point of Turing’s paper), it can give an insight into the way in which higher order information arises and leads to loss of computational control—while demonstrating how the control can be re-established, in special circumstances, via suitable type reductions. We examine the classical computational framework more closely than is usual, drawing out lessons for the wider application of information–theoretical approaches to characterizing the real world. The problem which arises across a range of contexts is the characterizing of the balance of power between the complexity of informational structure (with emergence, chaos, randomness and ‘big data’ prominently on the scene) and the means available (simulation, codes, statistical sampling, human intuition, semantic constructs) to bring this information back into the computational fold. We proceed via appropriate mathematical modelling to a more coherent view of the computational structure of information, relevant to a wide spectrum of areas of investigation
Randomness and Computability
This thesis establishes significant new results in the area of algorithmic randomness.
These results elucidate the deep relationship between randomness
and computability.
A number of results focus on randomness for finite strings. Levin introduced
two functions which measure the randomness of finite strings. One
function is derived from a universal monotone machine and the other function
is derived from an optimal computably enumerable semimeasure. Gacs
proved that infinitely often, the gap between these two functions exceeds the
inverse Ackermann function (applied to string length). This thesis improves
this result to show that infinitely often the difference between these two functions
exceeds the double logarithm. Another separation result is proved for
two different kinds of process machine.
Information about the randomness of finite strings can be used as a computational
resource. This information is contained in the overgraph. Muchnik
and Positselsky asked whether there exists an optimal monotone machine
whose overgraph is not truth-table complete. This question is answered in the
negative. Related results are also established.
This thesis makes advances in the theory of randomness for infinite binary
sequences. A variant of process machines is used to characterise computable
randomness, Schnorr randomness and weak randomness. This result is extended
to give characterisations of these types of randomness using truthtable
reducibility. The computable Lipschitz reducibility measures both the
relative randomness and the relative computational power of real numbers. It
is proved that the computable Lipschitz degrees of computably enumerable
sets are not dense.
Infinite binary sequences can be regarded as elements of Cantor space.
Most research in randomness for Cantor space has been conducted using the
uniform measure. However, the study of non-computable measures has led to
interesting results. This thesis shows that the two approaches that have been
used to define randomness on Cantor space for non-computable measures:
that of Reimann and Slaman, along with the uniform test approach first introduced
by Levin and also used by Gacs, Hoyrup and Rojas, are equivalent.
Levin established the existence of probability measures for which all infinite sequences are random. These measures are termed neutral measures. It is
shown that every PA degree computes a neutral measure. Work of Miller is
used to show that the set of atoms of a neutral measure is a countable Scott set
and in fact any countable Scott set is the set of atoms of some neutral measure.
Neutral measures are used to prove new results in computability theory. For
example, it is shown that the low computable enumerable sets are precisely
the computably enumerable sets bounded by PA degrees strictly below the
halting problem.
This thesis applies ideas developed in the study of randomness to computability
theory by examining indifferent sets for comeager classes in Cantor
space. A number of results are proved. For example, it is shown that there
exist 1-generic sets that can compute their own indifferent sets
Learning Efficient Disambiguation
This dissertation analyses the computational properties of current
performance-models of natural language parsing, in particular Data Oriented
Parsing (DOP), points out some of their major shortcomings and suggests
suitable solutions. It provides proofs that various problems of probabilistic
disambiguation are NP-Complete under instances of these performance-models, and
it argues that none of these models accounts for attractive efficiency
properties of human language processing in limited domains, e.g. that frequent
inputs are usually processed faster than infrequent ones. The central
hypothesis of this dissertation is that these shortcomings can be eliminated by
specializing the performance-models to the limited domains. The dissertation
addresses "grammar and model specialization" and presents a new framework, the
Ambiguity-Reduction Specialization (ARS) framework, that formulates the
necessary and sufficient conditions for successful specialization. The
framework is instantiated into specialization algorithms and applied to
specializing DOP. Novelties of these learning algorithms are 1) they limit the
hypotheses-space to include only "safe" models, 2) are expressed as constrained
optimization formulae that minimize the entropy of the training tree-bank given
the specialized grammar, under the constraint that the size of the specialized
model does not exceed a predefined maximum, and 3) they enable integrating the
specialized model with the original one in a complementary manner. The
dissertation provides experiments with initial implementations and compares the
resulting Specialized DOP (SDOP) models to the original DOP models with
encouraging results.Comment: 222 page