Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming

Methods based on Rapidly-exploring Random Trees (RRTs) have been widely used in robotics to solve motion planning problems. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool for simulating computations of one and only one model that has been hard-coded. The main contribution of this paper with respect to [21] is the introduction of a novel solution for membrane computing simulators based on automatic programming. First, we have extended the P-Lingua syntax –a language to define membrane computing models– to write RENPSM models. Second, we have implemented a new parser based on Flex and Bison to read RENPSM models and produce source code in C language for multicore processors with OpenMP. Finally, additional experiments are presented.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

Técnico en Sistemas Microinformáticos y Redes. Módulo profesional aplicaciones ofimáticas. Unidad didáctica : crea hojas de cálculo excelentes

Máster Universitario en Formación del Profesorado de ESO, Bachillerato, Formación Profesional y Enseñanza de Idiomas. Especialidad en Tecnología (M090

A syntax for semantics in P-Lingua

P-Lingua is a software framework for Membrane Computing, it includes a programming language, also called P-Lingua, for writting P system de nitions using a syntax close to standard scienti c notation. The rst line of a P-Lingua le is an unique identi er de ning the variant or model of P system to be used, i.e, the semantics of the P system. Software tools based on P-Lingua use this identi er to select a simulation algorithm implementing the corresponding derivation mode. Derivation modes de ne how to obtain a con guration Ct+1 from a con guration Ct. This information is usually hard-coded in the simulation algorithm. The P system model also de nes what types or rules can be used, the P-Lingua compiler uses the identi er to select an speci c parser for the le. In this case, a set of parsers is codi ed within the compiler tool. One for each unique identi er. P-Lingua has grown during the last 12 years, including more and more P system models. From a software engineering point of view, this approximation implies a continous development of the framework, leading to a monolithic software which is hard to debug and maintain. In this paper, we propose a new software approximation for the framework, including a new syntax for de ning rule patterns and derivation modes. The P-Lingua users can now de ne custom P system models instead of hard-coding them in the software. This approximation leads to a more exible solution which is easier to maintain and debug. Moreover, users could de ne and play with new/experimental P system models

New applications for an old tool

First, the dependency graph technique, not so far from its current application, was developed trying to nd the shortest computations for membrane systems solving instances of SAT. Certain families of membrane systems have been demonstrated to be non-effcient by means of the reduction of nding an accepting computation (respectively, rejecting computation) to the problem of reaching from a node of the dependency graph to another one. In this paper, a novel application to this technique is explained. Supposing that a problem can be solved by means of a kind of membrane systems leads to a contradiction by means of using the dependency graph as a reasoning method. In this case, it is demonstrated that a single system without dissolution, polarizations and cooperation cannot distinguish a single object from more than one object. An extended version of this work will be presented in the 20th International Conference on Membrane Computing.Ministerio de Industria, Economía y Competitividad TIN2017-89842-

Dependency Graph Technique Revisited

The dependency graph technique was initially thought as a method to find short paths in the computation tree of a membrane system using weak metrics. It could be used to obtain reasonably fast SAT-solvers, capable of competing with the ones available in the literature. Later on, they were used as a method to demonstrate the non-efficiency of some membrane systems, capturing the dynamics of the systems by a static directed graph structure. Recently, the dependency graphs have also been used to establish negative results in Membrane Computing. Specifically, in this work, demonstrating the inability of a kind of membrane system to solve some decision problems efficiently by means of a single system.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

From NP-Completeness to DP-Completeness: A Membrane Computing Perspective

Presumably efficient computing models are characterized by their capability to provide polynomial-time solutions for NPcomplete problems. Given a classRof recognizer membrane systems,Rdenotes the set of decision problems solvable by families from R in polynomial time and in a uniform way. PMCR is closed under complement and under polynomial-time reduction. +erefore, if R is a presumably efficient computing model of recognizer membrane systems, then NP ∪ co-NP ⊆PMCR. In this paper, the lower bound NP ∪ co-NP for the time complexity class PMCR is improved for any presumably efficient computing model R of recognizer membrane systems verifying some simple requirements. Specifically, it is shown that DP ∪ co-DP is a lower bound for such PMCR, where DP is the class of differences of any two languages in NP. Since NP ∪ co-NP⊆DP ∩ co- DP, this lower bound for PMCR delimits a thinner frontier than that with NP ∪ co-NP.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

A new P-Lingua toolkit for agile development in membrane computing

Membrane computing is a massively parallel and non-deterministic bioinspired computing paradigm whose models are called P systems. Validating and testing such models is a challenge which is being overcome by developing simulators. Regardless of their heterogeneity, such simulators require to read and interpret the models to be simulated. To this end, P-Lingua is a high-level P system definition language which has been widely used in the last decade. The P-Lingua ecosystem includes not only the language, but also libraries and software tools for parsing and simulating membrane computing models. Each version of P-Lingua supported new types or variants of P systems. This leads to a shortcoming: Only a predefined list of variants can be used, thus making it difficult for researchers to study custom ones. Moreover, derivation modes cannot be user-defined, i.e, the way in which P system computations should be generated is determined by the simulation algorithm in the source code. The main contribution of this paper is a completely new design of the P-Lingua language, called P-Lingua 5, in which the user can define custom variants and derivation modes, among other improvements such as including procedural programming and simulation directives. It is worth mentioning that it has backward-compatibility with previous versions of the language. A completely new set of command-line tools is provided for parsing and simulating P-Lingua 5 files. Finally, several examples are included in this paper covering the most common P system types.Agencia Estatal de Investigación TIN2017-89842-

Design of Specific P Systems Simulators on GPUs

In order to validate P system models and to assist on their formal verification, simulators are indispensable. Moreover, having effi-cient simulation tools is crucial, and for this purpose, parallel platforms should be employed. So far, several parallel simulators for P systems have been developed, specifically targeting GPUs (Graphics Processing Units). Although being a hot topic within Membrane Computing, map-ping P system parallelism on GPUs is still not a mature area. In the past, we have successfully accelerated the simulation of two specific fam-ilies of P systems solving SAT with GPUs, and learned in the process some semantics ingredients that fit well on these parallel devices. We are extending this exploration by designing an specific simulator of a P system model for the FACTORIZATION problem. In this paper, we analyse the two main approaches for simulators, and depict some design decisions required for this case study.Ministerio de Industria, Economía y Competitividad TIN2017-89842-

Dendrite P Systems Toolbox: Representation, Algorithms and Simulators

Dendrite P systems (DeP systems) are a recently introduced neural-like model of computation. They provide an alternative to the more classical spiking neural (SN) P systems. In this paper, we present the first software simulator for DeP systems, and we investigate the key features of the representation of the syntax and semantics of such systems. First, the conceptual design of a simulation algorithm is discussed. This is helpful in order to shade a light on the differences with simulators for SN P systems, and also to identify potential parallelizable parts. Second, a novel simulator implemented within the PLingua simulation framework is presented. Moreover, MeCoSim, a GUI tool for abstract representation of problems based on P system models has been extended to support this model. An experimental validation of this simulator is also covered.Ministerio de Economía, Industria y Competitividad TIN2017-89842-P (MABICAP

Simulation of Computing P Systems: A GPU Design for the Factorization Problem

Ministerio de Economía, Industria y Competitividad TIN2017-89842-P (MABICAP)Ministerio de Economía y Competitividad TIN2015-71562-RED