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Are there intelligent Turing machines?  [PDF]
Norbert Bátfai
Computer Science , 2015,
Abstract: This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior. Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quotient or the emotional intelligence quotient for Turing machines.
Internal Turing Machines  [PDF]
Ken Loo
Mathematics , 2004,
Abstract: Using nonstandard analysis, we will extend the classical Turing machines into the internal Turing machines. The internal Turing machines have the capability to work with infinite ($*$-finite) number of bits while keeping the finite combinatoric structures of the classical Turing machines. We will show the following. The internal deterministic Turing machines can do in $*$-polynomial time what a classical deterministic Turing machine can do in an arbitrary finite amount of time. Given an element of $\in HALT$ (more precisely, the $*$-embedding of $HALT$), there is an internal deterministic Turing machine which will take $$ as input and halt in the $"yes"$ state. The language ${}^*Halt$ can not be decided by the internal deterministic Turing machines. The internal deterministic Turing machines can be viewed as the asymptotic behavior of finite precision approximation to real number computations. It is possible to use the internal probabilistic Turing machines to simulate finite state quantum mechanics with infinite precision. This simulation suggests that no information can be transmitted instantaneously and at the same time, the Turing machine model can simulate instantaneous collapse of the wave function. The internal deterministic Turing machines are powerful, but if $P \neq NP$, then there are internal problems which the internal deterministic Turing machines can solve but not in $*$-polynomial time.
Turing Automata and Graph Machines
Miklós Bartha
Electronic Proceedings in Theoretical Computer Science , 2010, DOI: 10.4204/eptcs.26.3
Abstract: Indexed monoidal algebras are introduced as an equivalent structure for self-dual compact closed categories, and a coherence theorem is proved for the category of such algebras. Turing automata and Turing graph machines are defined by generalizing the classical Turing machine concept, so that the collection of such machines becomes an indexed monoidal algebra. On the analogy of the von Neumann data-flow computer architecture, Turing graph machines are proposed as potentially reversible low-level universal computational devices, and a truly reversible molecular size hardware model is presented as an example.
Ambiguity of ω-Languages of Turing Machines  [PDF]
Olivier Finkel
Mathematics , 2012, DOI: 10.2168/LMCS-10(3:12)2014
Abstract: An {\omega}-language is a set of infinite words over a finite alphabet X. We consider the class of recursive {\omega}-languages, i.e. the class of {\omega}-languages accepted by Turing machines with a B\"uchi acceptance condition, which is also the class {\Sigma}11 of (effective) analytic subsets of X{\omega} for some finite alphabet X. We investigate here the notion of ambiguity for recursive {\omega}-languages with regard to acceptance by B\"uchi Turing machines. We first present in detail essentials on the literature on {\omega}-languages accepted by Turing Machines. Then we give a complete and broad view on the notion of ambiguity and unambiguity of B\"uchi Turing machines and of the {\omega}-languages they accept. To obtain our new results, we make use of results and methods of effective descriptive set theory.
Beyond Turing Machines  [PDF]
Kurt Ammon
Computer Science , 2009,
Abstract: This paper discusses "computational" systems capable of "computing" functions not computable by predefined Turing machines if the systems are not isolated from their environment. Roughly speaking, these systems can change their finite descriptions by interacting with their environment.
Some relations between quantum Turing machines and Turing machines  [PDF]
Andrés Sicard,Mario Vélez
Physics , 1999,
Abstract: For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.
Decision Problems For Turing Machines  [PDF]
Olivier Finkel,Dominique Lecomte
Mathematics , 2009,
Abstract: We answer two questions posed by Castro and Cucker, giving the exact complexities of two decision problems about cardinalities of omega-languages of Turing machines. Firstly, it is $D_2(\Sigma_1^1)$-complete to determine whether the omega-language of a given Turing machine is countably infinite, where $D_2(\Sigma_1^1)$ is the class of 2-differences of $\Sigma_1^1$-sets. Secondly, it is $\Sigma_1^1$-complete to determine whether the omega-language of a given Turing machine is uncountable.
Small weakly universal Turing machines  [PDF]
Turlough Neary,Damien Woods
Computer Science , 2007,
Abstract: We give small universal Turing machines with state-symbol pairs of (6, 2), (3, 3) and (2, 4). These machines are weakly universal, which means that they have an infinitely repeated word to the left of their input and another to the right. They simulate Rule 110 and are currently the smallest known weakly universal Turing machines.
Neural Turing Machines  [PDF]
Alex Graves,Greg Wayne,Ivo Danihelka
Computer Science , 2014,
Abstract: We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.
Infinite Time Turing Machines  [PDF]
Joel David Hamkins,Andy Lewis
Mathematics , 1998,
Abstract: We extend in a natural way the operation of Turing machines to infinite ordinal time, and investigate the resulting supertask theory of computability and decidability on the reals. The resulting computability theory leads to a notion of computation on the reals and concepts of decidability and semi-decidability for sets of reals as well as individual reals. Every Pi^1_1 set, for example, is decidable by such machines, and the semi-decidable sets form a portion of the Delta^1_2 sets. Our oracle concept leads to a notion of relative computability for reals and sets of reals and a rich degree structure, stratified by two natural jump operators.
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