Showing papers on "Turing machine published in 1973"

Logical reversibility of computation

Abstract: The usual general-purpose computing automaton (e.g.. a Turing machine) is logically irreversible- its transition function lacks a single-valued inverse. Here it is shown that such machines may he made logically reversible at every step, while retainillg their simplicity and their ability to do general computations. This result is of great physical interest because it makes plausible the existence of thermodynamically reversible computers which could perform useful computations at useful speed while dissipating considerably less than kT of energy per logical step. In the first stage of its computation the logically reversible automaton parallels the corresponding irreversible automaton, except that it saves all intermediate results, there by avoiding the irreversible operation of erasure. The second stage consists of printing out the desired output. The third stage then reversibly disposes of all the undesired intermediate results by retracing the steps of the first stage in backward order (a process which is only possible because the first stage has been carried out reversibly), there by restoring the machine (except for the now-written output tape) to its original condition. The final machine configuration thus contains the desired output and a reconstructed copy of the input, but no other undesired data. The foregoing results are demonstrated explicitly using a type of three-tape Turing machine. The biosynthesis of messenger RNA is discussed as a physical example of reversible computation.

The Hardest Context-Free Language

Abstract: There is a context-free language $L_0 $ such that every context-free language is an inverse homomorphic image of $L_0 $ or $L_0 - \{ e\} $. Hence the time complexity of recognition of $L_0 $ is the least upper bound for time complexity of recognition of context-free languages. A similar result holds for quasirealtime Turing machine languages. Several languages are given such that deterministic and nondeterministic polynomial time acceptance are equivalent if and only if any one of them is deterministic polynomial time acceptable.

Solvability of the halting problem for certain classes of Turing machines

Abstract: One method of proving that some Turing machine is not universal is to prove that the halting problem is solvable for it. Therefore, to obtain a lower bound on the complexity of a universal machine, it is convenient to have a criterion of solvability of the halting problem. In the present paper, we establish some of these criteria; they are formulated in terms of properties of machine graphs and computations.

On tape-bounded complexity classes and multi-head finite automata

Abstract: The principal result described in this paper is the equivalence of the following statements: (1) Every set accepted by a nondeterministic one-way two-head finite automaton can be accepted by a deterministic two-way k-head finite automaton, for some k. (2) The context-free language LPΣ (described in the paper) is recognized by a (log n)-tape bounded deterministic Turing machine. (3) Every set accepted by a L(n)-tape bounded nondeterministic Turing machine is recognized by a L(n)-tape bounded deterministic Turing machine, provided L(n) ≥ log n. This work extends results reported earlier by Hartmanis [2], Savitch [9,10], and Lewis, Stearns, and Hartmanis [6].

Many-one degrees associated with problems of tag

Abstract: Tag systems were defined by Post [9], [10] and have been studied by a number of researchers including Minsky [7], Maslov [6] and Aanderaa and Belsnes [1]. In their recent paper Aanderaa and Belsnes demonstrated that every r.e. many-one degree (exclusive of the degree of the empty set) is represented by the general halting problem for tag systems, that is, by the family of halting problems ranging over all tag systems. Their result depends upon an informal proof of this property for Turing machines but may be seen to be correct in light of a formal proof due to Overbeek [8]. Our aim is to extend their results to the general word problem for these systems. Specifically, we shall present an effective method which, when applied to an arbitrary r.e. set S , where S is neither empty nor the set of all natural numbers, produces a tag system R ′ whose word and halting problems are both of the same many-one degree as the decision problem for S . The proof is realized by first constructing, from the description of an arbitrary Turing machine M , which machine has at least one mortal and one immortal configuration, a 5-register machine R , whose word and halting problems are both of the same many-one degree as the halting problem for M . From R we then construct the desired tag system R ′. This construction combined with Overbeek's [8] shows that every r.e. many-one degree (exclusive of the degrees of the empty set and the set of all natural numbers) is represented by the general word and halting problems for tag systems. Moreover our results are seen to be best possible with regard to degrees of unsolvability in that it is not the case that every nonrecursive r.e. one-one degree is represented by either of the general decision problems for tag systems which are considered here. These results were first shown in the author's thesis [3] and were announced in [4], They form part of an extensive study into the many-one equivalence of general decision problems. An overview of the initial findings of this research project may be found in [5].

On Controlled and Totally Neural-Replies Generated Concepts for Biology and Functional Brain Theory

Abstract: The control of concepts and conceptual coherence in biology and functional brain theory has to be extended by dimensional analysis. Controlled concepts have to be applied within closed and verifiable methodological circuits. Dimensional analysis has to be extended to various of its identity cases. The generalizing extension requires a new logic having a heterogeneously interpreted categorization of physical basic domains as its foundation. Central to this logic is the concept of heterogeneous coordinative relation. The scheme of this relation generates a physical neuron and a physically generalized Turing machine. The theory of the total reduction to a single physical ultimate demonstrates how physically important scientific key concepts are to be generated: either by a physically back-connected neuron or by the physically generalized Turing machine. As the human brain applies physical neurons by means of the above mentioned method concept generation and transformation can be simulated: but also the concept generating possibilities of the human brain can be optimized and the process of programming brain-like systems can be explained.