4,043 research outputs found
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
Fast Hardware Implementations of Static P Systems
In this article we present a simulator of non-deterministic static P systems
using Field Programmable Gate Array (FPGA) technology. Its major feature
is a high performance, achieving a constant processing time for each transition. Our
approach is based on representing all possible applications as words of some regular
context-free language. Then, using formal power series it is possible to obtain the
number of possibilities and select one of them following a uniform distribution, in
a fair and non-deterministic way. According to these ideas, we yield an implementation
whose results show an important speed-up, with a strong independence from
the size of the P system.Ministry of Science and Innovation of the Spanish Government under the project TEC2011-27936 (HIPERSYS)European Regional Development Fund (ERDF)Ministry of Education of Spain (FPU grant AP2009-3625)ANR project SynBioTI
Generalized Communicating P Systems Working in Fair Sequential Model
In this article we consider a new derivation mode for generalized
communicating P systems (GCPS) corresponding to the functioning of population
protocols (PP) and based on the sequential derivation mode and a fairness
condition. We show that PP can be seen as a particular variant of GCPS. We also
consider a particular stochastic evolution satisfying the fairness condition
and obtain that it corresponds to the run of a Gillespie's SSA. This permits to
further describe the dynamics of GCPS by a system of ODEs when the population
size goes to the infinity.Comment: Presented at MeCBIC 201
Generating Diophantine Sets by Virus Machines
Virus Machines are a computational paradigm inspired by
the manner in which viruses replicate and transmit from one host cell to
another. This paradigm provides non-deterministic sequential devices.
Non-restricted virus machines are unbounded virus machines, in the
sense that no restriction on the number of hosts, the number of instructions
and the number of viruses contained in any host along any computation
is placed on them. The computational completeness of these
machines has been obtained by simulating register machines. In this
paper, virus machines as set generating devices are considered. Then,
the universality of non-restricted virus machines is proved by showing
that they can compute all diophantine sets, which the MRDP theorem
proves that coincide with the recursively enumerable sets.Ministerio de Economía y Competitividad TIN2012- 3743
Computing Partial Recursive Functions by Virus Machines
Virus Machines are a computational paradigm inspired by
the manner in which viruses replicate and transmit from one host cell to
another. This paradigm provides non-deterministic sequential devices.
Non-restricted Virus Machines are unbounded Virus Machines, in the
sense that no restriction on the number of hosts, the number of instructions
and the number of viruses contained in any host along any computation
is placed on them. The computational completeness of these
machines has been obtained by simulating register machines. In this
paper, Virus Machines as function computing devices are considered.
Then, the universality of non-restricted virus machines is proved by showing
that they can compute all partial recursive functions.Ministerio de Economía y Competitividad TIN2012- 3743
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
Computing with cells: membrane systems - some complexity issues.
Membrane computing is a branch of natural computing which abstracts computing models from the structure and the functioning of the living cell. The main ingredients of membrane systems, called P systems, are (i) the membrane structure, which consists of a hierarchical arrangements of membranes which delimit compartments where (ii) multisets of symbols, called objects, evolve according to (iii) sets of rules which are localised and associated with compartments. By using the rules in a nondeterministic/deterministic maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one membrane to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. Membrane systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future biotechnology gives way to a practical bio-realization. In this paper we survey some interesting and fundamental complexity issues such as universality vs. nonuniversality, determinism vs. nondeterminism, membrane and alphabet size hierarchies, characterizations of context-sensitive languages and other language classes and various notions of parallelism
A Formal Framework for P Systems with Dynamic Structure
This article introduces a formalism/framework able to describe different
variants of P systems having a dynamic structure. This framework can be useful for
the definition of new variants of P systems with dynamic structure, for the comparison
of existing definitions as well as for their extension. We give a precise definition of the
formalism and show how existing variants of P systems with dynamic structure can be
translated to it
Structured Modeling with Hyperdag P Systems: Part A
P systems provide a computational model based on the structure and interaction
of living cells. A P system consists of a hierarchical nesting of cell-like
membranes, which can be visualized as a rooted tree.
Although the P systems are computationally complete, many real world models, e.g.,
from socio-economic systems, databases, operating systems, distributed systems, seem to
require more expressive power than provided by tree structures. Many such systems have a
primary tree-like structure completed with shared or secondary communication channels.
Modeling these as tree-based systems, while theoretically possible, is not very appealing,
because it typically needs artificial extensions that introduce additional complexities,
nonexistent in the originals.
In this paper we propose and define a new model that combines structure and flexibility,
called hyperdag P systems, in short, hP systems, which extend the definition of
conventional P systems, by allowing dags, interpreted as hypergraphs, instead of trees,
as models for the membrane structure.
We investigate the relation between our hP systems and neural P systems. Despite
using an apparently less powerful structure, i.e., a dag instead of a general graph, we
argue that hP systems have essentially the same computational power as tissue and neural
P systems. We argue that hP systems offer a structured approach to membrane-based
modeling that is often closer to the behavior and underlying structure of the modeled
objects.
Additionally, we enable dynamical changes of the rewriting modes (e.g., to alternate
between determinism and parallelism) and of the transfer modes (e.g., the switch between
unicast or broadcast). In contrast, classical P systems, both tree and graph based
P systems, seem to focus on a statical approach.
We support our view with a simple but realistic example, inspired from computer
networking, modeled as a hP system with a shared communication line (broadcast channel).
In Part B of this paper we will explore this model further and support it with a
more extensive set of examples
Semilinear Sets, Register Machines, and Integer Vector Addition (P) Systems
In this paper we consider P systems working with multisets with integer
multiplicities. We focus on a model in which rule applicability is not in
uenced by the
contents of the membrane. We show that this variant is closely related to blind register
machines and integer vector addition systems. Furthermore, we describe the computational
power of these models in terms of linear and semilinear sets of integer vectors
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