182 research outputs found
On the boundaries of solvability and unsolvability in tag systems. Theoretical and Experimental Results
Several older and more recent results on the boundaries of solvability and
unsolvability in tag systems are surveyed. Emphasis will be put on the
significance of computer experiments in research on very small tag systems
A Stochastic Complexity Perspective of Induction in Economics and Inference in Dynamics
Rissanen's fertile and pioneering minimum description length principle (MDL) has been viewed from the point of view of statistical estimation theory, information theory, as stochastic complexity theory -.i.e., a computable approximation to Kolomogorov Complexity - or Solomonoff's recursion theoretic induction principle or as analogous to Kolmogorov's sufficient statistics. All these - and many more - interpretations are valid, interesting and fertile. In this paper I view it from two points of view: those of an algorithmic economist and a dynamical system theorist. >From these points of view I suggest, first, a recasting of Jevons's sceptical vision of induction in the light of MDL; and a complexity interpretation of an undecidable question in dynamics.
PAC-learning is Undecidable
The problem of attempting to learn the mapping between data and labels is the
crux of any machine learning task. It is, therefore, of interest to the machine
learning community on practical as well as theoretical counts to consider the
existence of a test or criterion for deciding the feasibility of attempting to
learn. We investigate the existence of such a criterion in the setting of
PAC-learning, basing the feasibility solely on whether the mapping to be learnt
lends itself to approximation by a given class of hypothesis functions. We show
that no such criterion exists, exposing a fundamental limitation in the
decidability of learning. In other words, we prove that testing for
PAC-learnability is undecidable in the Turing sense. We also briefly discuss
some of the probable implications of this result to the current practice of
machine learning
Power of Randomization in Automata on Infinite Strings
Probabilistic B\"uchi Automata (PBA) are randomized, finite state automata
that process input strings of infinite length. Based on the threshold chosen
for the acceptance probability, different classes of languages can be defined.
In this paper, we present a number of results that clarify the power of such
machines and properties of the languages they define. The broad themes we focus
on are as follows. We present results on the decidability and precise
complexity of the emptiness, universality and language containment problems for
such machines, thus answering questions central to the use of these models in
formal verification. Next, we characterize the languages recognized by PBAs
topologically, demonstrating that though general PBAs can recognize languages
that are not regular, topologically the languages are as simple as
\omega-regular languages. Finally, we introduce Hierarchical PBAs, which are
syntactically restricted forms of PBAs that are tractable and capture exactly
the class of \omega-regular languages
Probabilistic description logics for subjective uncertainty
We propose a family of probabilistic description logics (DLs) that are derived in a principled way from Halpern's probabilistic first-order logic. The resulting probabilistic DLs have a two-dimensional semantics similar to temporal DLs and are well-suited for representing subjective probabilities. We carry out a detailed study of reasoning in the new family of logics, concentrating on probabilistic extensions of the DLs ALC and EL, and showing that the complexity ranges from PTime via ExpTime and 2ExpTime to undecidable
Generic algorithms for halting problem and optimal machines revisited
The halting problem is undecidable --- but can it be solved for "most"
inputs? This natural question was considered in a number of papers, in
different settings. We revisit their results and show that most of them can be
easily proven in a natural framework of optimal machines (considered in
algorithmic information theory) using the notion of Kolmogorov complexity. We
also consider some related questions about this framework and about asymptotic
properties of the halting problem. In particular, we show that the fraction of
terminating programs cannot have a limit, and all limit points are Martin-L\"of
random reals. We then consider mass problems of finding an approximate solution
of halting problem and probabilistic algorithms for them, proving both positive
and negative results. We consider the fraction of terminating programs that
require a long time for termination, and describe this fraction using the busy
beaver function. We also consider approximate versions of separation problems,
and revisit Schnorr's results about optimal numberings showing how they can be
generalized.Comment: a preliminary version was presented at the ICALP 2015 conferenc
Recurrent Neural Networks as Weighted Language Recognizers
We investigate the computational complexity of various problems for simple
recurrent neural networks (RNNs) as formal models for recognizing weighted
languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs
with softmax, which are commonly used in natural language processing
applications. We show that most problems for such RNNs are undecidable,
including consistency, equivalence, minimization, and the determination of the
highest-weighted string. However, for consistent RNNs the last problem becomes
decidable, although the solution length can surpass all computable bounds. If
additionally the string is limited to polynomial length, the problem becomes
NP-complete and APX-hard. In summary, this shows that approximations and
heuristic algorithms are necessary in practical applications of those RNNs
Computability and Algorithmic Complexity in Economics
This is an outline of the origins and development of the way computability theory and algorithmic complexity theory were incorporated into economic and finance theories. We try to place, in the context of the development of computable economics, some of the classics of the subject as well as those that have, from time to time, been credited with having contributed to the advancement of the field. Speculative thoughts on where the frontiers of computable economics are, and how to move towards them, conclude the paper. In a precise sense - both historically and analytically - it would not be an exaggeration to claim that both the origins of computable economics and its frontiers are defined by two classics, both by Banach and Mazur: that one page masterpiece by Banach and Mazur ([5]), built on the foundations of Turing’s own classic, and the unpublished Mazur conjecture of 1928, and its unpublished proof by Banach ([38], ch. 6 & [68], ch. 1, #6). For the undisputed original classic of computable economics is RabinÃs effectivization of the Gale-Stewart game ([42];[16]); the frontiers, as I see them, are defined by recursive analysis and constructive mathematics, underpinning computability over the computable and constructive reals and providing computable foundations for the economist’s Marshallian penchant for curve-sketching ([9]; [19]; and, in general, the contents of Theoretical Computer Science, Vol. 219, Issue 1-2). The former work has its roots in the Banach-Mazur game (cf. [38], especially p.30), at least in one reading of it; the latter in ([5]), as well as other, earlier, contributions, not least by Brouwer.
From Traditional Set Theory – that of Cantor, Hilbert , Gödel, Cohen – to Its Necessary Quantum Extension
The original purpose of the present study, 2011, started with a preprint «On the Probable Failure of the Uncountable Power Set Axiom», 1988, is to save from the transfinite deadlock of higher set theory the jewel of mathematical Continuum — this genuine, even if mostly forgotten today raison d’être of all traditional set-theoretical enterprises to Infinity and beyond, from Georg Cantor to David Hilbert to Kurt Gödel to W. Hugh Woodin to Buzz Lightyear
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