Solving SAT in linear time with a neural-like membrane system

We present in this paper a neural-like membrane system solving the SAT problem in linear time. These neural Psystems are nets of cells working with multisets. Each cell has a finite state memory, processes multisets of symbol-impulses, and can send impulses (?excitations?) to the neighboring cells. The maximal mode of rules application and the replicative mode of communication between cells are at the core of the eficiency of these systems

Multi-Head Finite Automata: Characterizations, Concepts and Open Problems

Multi-head finite automata were introduced in (Rabin, 1964) and (Rosenberg, 1966). Since that time, a vast literature on computational and descriptional complexity issues on multi-head finite automata documenting the importance of these devices has been developed. Although multi-head finite automata are a simple concept, their computational behavior can be already very complex and leads to undecidable or even non-semi-decidable problems on these devices such as, for example, emptiness, finiteness, universality, equivalence, etc. These strong negative results trigger the study of subclasses and alternative characterizations of multi-head finite automata for a better understanding of the nature of non-recursive trade-offs and, thus, the borderline between decidable and undecidable problems. In the present paper, we tour a fragment of this literature

A new class of symbolic abstract neural nets

Starting from the way the inter-cellular communication takes place by means of protein channels and also from the standard knowledge about neuron functioning, we propose a computing model called a tissue P system, which processes symbols in a multiset rewriting sense, in a net of cells similar to a neural net. Each cell has a finite state memory, processes multisets of symbol-impulses, and can send impulses (?excitations?) to the neighboring cells. Such cell nets are shown to be rather powerful: they can simulate a Turing machine even when using a small number of cells, each of them having a small number of states. Moreover, in the case when each cell works in the maximal manner and it can excite all the cells to which it can send impulses, then one can easily solve the Hamiltonian Path Problem in linear time. A new characterization of the Parikh images of ET0L languages are also obtained in this framework

Automata Systems

Táto bakalárska práca sa zaoberá automatovými systémami, konkrétne definuje stavovo a pravidlovo kontrolované paralelné typy automatových systémov, ktorých komponentmi sú konečné automaty, na základe rozboru a skúmania princípov už existujúcich systémov. Taktiež sú práci porovnávané tieto nové systémy s týmy existujúcimi, sú skúmané ich vlastnosti a možnosť transformácie stavovo kontrolovaného paralelného automatového systému na pravidlovo kontrolovaný paralelný automatový systém a aj opačne.This bachelor thesis deals with automata systems, especially the ones, which are defined as state and or rule controlled parallel automata systems. Their components are finite automata systems. Definitions are retrieved from analyzing and researching already existing automata systems. In the thesis, a comparison of these systems is made with systems defined a priori. The thesis also analyzes first features of these systems, and second a possibilities of transforming the state controlled parallel automata system into the rule controlled parallel automata system and vice versa.

On State-Synchronized Automata Systems

In this paper, we introduce a new kind of automata systems, called state-synchronized automata systems of degree n. In general, they consists of n pushdown automata, referred to as their components. These systems can perform a computation step provided that the concatenation of the current states of all their components belongs to a prescribed control language. As its main result, the paper demonstrates that these systems characterize the family of recursively enumerable languages. In fact, this characterization is demostrated in both deterministic and nondeterministic versions of these systems. Restricting their components, these systems provides less computational power