Vol. 27 · No. 3+

Starting from Shannon theory of information, this paper presents the case of producing information in the form of multisets, and encoding information using multisets. We review the entropy rate of a multiset information source and derive a formula for the information content of a multiset. Then we study the encoder and channel part of the system, obtaining some results about multiset encoding length and channel capacity.

We consider here a variant of rewriting P systems citecond, where communication is controlled by the contents of the strings, not by the evolution rules used for obtaining these strings. Some new characterizations of recursively enumerable languages are obtained by means of P systems with a small number of membranes, which improves some of the known results from citecond and citemadhu.

We define a model of membrane system where each membrane is clocked independently from the others, in the sense that every derivation step is applied without a global synchronization. The computation is obtained by the execution of a limited amount of rules in each membrane, and only when they are allowed to execute a derivation step. Indeed, each membrane operates with a certain work frequency that can change across the system. Simple results show that this model is at least as powerful as the usual one, and the goal is to present a few examples that show it giving rise to interesting dynamic behaviors.

We study P colonies introduced in citekel2 as a class of abstract computing devices composed of independent membrane agents, acting and evolving in a shared environment. In the present paper especially P colonies are considered, which are homogeneous with respect to the type of rules in each program of agents. The number of agents, as well as the number of programs in each agent are bounded, which are sufficient to guarantee computational completeness of homogeneous P colonies. We present results for P colonies with one and with two objects inside each agent.

In this paper we approach the problem of hierarchical clustering through membrane computing. A specific P system with external output is designed for each Boolean matrix associated with a finite set of individuals. The computation of the system allows us to obtain one of the possible classifications in a non-deterministic way. The amount of resources required in the construction is polynomial in the number of individuals and of characteristics analyzed.

In this paper we investigate some applications of spiking neural P systems regarding their capability to solve some classical computer science problems. In this respect versatility of such systems is studied to simulate a well known parallel computational model, namely the Boolean circuits. In addition, another notorious application – sorting – is considered within this framework.

There are two formalisms for simulating spatially homogeneous chemical system; the deterministic approach, usually based on differential equations (reaction rate equations) and the stochastic approach which is based on a single differential-difference equation (the master equation). The stochastic approach has a firmer physical basis than the deterministic approach, but the master equation is often mathematically intractable. Thus, a method was proposed to make exact numerical calculations within the framework of the stochastic formulation without having to deal with the master equation directly. However, its drawback remains in great amount of computer time that is often required to simulate a desired amount of system time. A novel method that we propose is Deterministic Abstract Rewriting System on Multisets (DARMS), which is a deterministic approach based on an approximate procedure of an exact stochastic method. DARMS can produce significant gains in simulation speed with acceptable losses in accuracy. DARMS is a class of P Systems in which reaction rules are applied in parallel and deterministically. The feasibility and utility of DARMS are demonstrated by applying it to the Oregonator, which is a well-known model of the Belousov-Zhabotinskii (BZ) reaction. We also consider 1-dimensional and 2-dimensional cellular automata composed of DARMS and confirm that it can exhibit typical pattern formations of the BZ reaction. Since DARMS is a deterministic approach, it ignores the inherent fluctuations and correlations in chemical reactions; they are not so significant in spatially homogeneous chemical reactions but significant in bio-chemical systems. Thus, we also propose a stochastic approach, Stochastic ARMS (SARMS); SARMS is not an exact stochastic approach, but an approximate procedure of the exact stochastic method.

Because of their inherent large-scale parallelism, membrane computing models can be fully exploited only through the use of a parallel computing platform. We have fully implemented such a computing platform based on reconfigurable hardware that is intended to support the efficient execution of membrane computing models. This computing platform is the first of its type to implement parallelism at both the system and region levels. In this paper, we describe how our computing platform implements the core features of membrane computing models in hardware, and present a theoretical performance analysis of the algorithm it executes in hardware. The performance analysis suggests that the computing platform can significantly outperform sequential implementations of membrane computing as well as Petreska and Teuscher's hardware implementation, the only other complete hardware implementation of membrane computing in existence.

This contribution argues for the proposition that formal models based on the theory of formal grammars and languages are adequate for the study of some computationally relevant properties of agents and multi-agent systems. Some questions are formulated concerning the possibilities to enlarge the universality and realism of such models by considering the possibilities to go with their computing abilities beyond the traditional Turing-computability, and by considering very natural properties of any real (multi-)agent system such as the partially predictable functioning (behavior) of agents, their unreliability, dysfunctions, etc.