39,436 research outputs found
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
Playing with Derivation Modes and Halting Conditions
In the area of P systems, besides the standard maximally parallel derivation
mode, many other derivation modes have been investigated, too. In this paper, many
variants of hierarchical P systems and tissue P systems using different derivation modes
are considered and the effects of using di erent derivation modes, especially the maximally
parallel derivation modes and the maximally parallel set derivation modes, on the
generative and accepting power are illustrated. Moreover, an overview on some control
mechanisms used for (tissue) P systems is given.
Furthermore, besides the standard total halting mode, we also consider different halting
conditions such as unconditional halting and partial halting and explain how the use
of different halting modes may considerably change the computing power of P systems
and tissue P systems
Types for BioAmbients
The BioAmbients calculus is a process algebra suitable for representing
compartmentalization, molecular localization and movements between
compartments. In this paper we enrich this calculus with a static type system
classifying each ambient with group types specifying the kind of compartments
in which the ambient can stay. The type system ensures that, in a well-typed
process, ambients cannot be nested in a way that violates the type hierarchy.
Exploiting the information given by the group types, we also extend the
operational semantics of BioAmbients with rules signalling errors that may
derive from undesired ambients' moves (i.e. merging incompatible tissues).
Thus, the signal of errors can help the modeller to detect and locate unwanted
situations that may arise in a biological system, and give practical hints on
how to avoid the undesired behaviour
One-Membrane P Systems with Activation and Blocking of Rules
We introduce new possibilities to control the application of rules based on
the preceding applications, which can be de ned in a general way for (hierarchical) P
systems and the main known derivation modes. Computational completeness can be
obtained even for one-membrane P systems with non-cooperative rules and using both
activation and blocking of rules, especially for the set modes of derivation.
When we allow the application of rules to in
uence the application of rules in previous
derivation steps, applying a non-conservative semantics for what we consider to be a
derivation step, we can even \go beyond Turing"
Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges
Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to
high complexity, interaction, parallelism and continuous change of roles and
organisation between agents. In this paper we record our research experience on
formal modelling of MAS. We review our research throughout the last decade, by
describing the problems we have encountered and the decisions we have made
towards resolving them and providing solutions. Much of this work involved
membrane computing and classes of P Systems, such as Tissue and Population P
Systems, targeted to the modelling of MAS whose dynamic structure is a
prominent characteristic. More particularly, social insects (such as colonies
of ants, bees, etc.), biology inspired swarms and systems with emergent
behaviour are indicative examples for which we developed formal MAS models.
Here, we aim to review our work and disseminate our findings to fellow
researchers who might face similar challenges and, furthermore, to discuss
important issues for advancing research on the application of membrane
computing in MAS modelling.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314
Proceedings of International Workshop "Global Computing: Programming Environments, Languages, Security and Analysis of Systems"
According to the IST/ FET proactive initiative on GLOBAL COMPUTING, the goal is to obtain techniques (models, frameworks, methods, algorithms) for constructing systems that are flexible, dependable, secure, robust and efficient.
The dominant concerns are not those of representing and manipulating data efficiently but rather those of handling the co-ordination and interaction, security, reliability, robustness, failure modes, and control of risk of the entities in the system and the overall design, description and performance of the system itself.
Completely different paradigms of computer science may have to be developed to tackle these issues effectively. The research should concentrate on systems having the following characteristics: • The systems are composed of autonomous computational entities where activity is not centrally controlled, either because global control is impossible or impractical, or because the entities are created or controlled by different owners.
• The computational entities are mobile, due to the movement of the physical platforms or by movement of the entity from one platform to another.
• The configuration varies over time. For instance, the system is open to the introduction of new computational entities and likewise their deletion.
The behaviour of the entities may vary over time.
• The systems operate with incomplete information about the environment.
For instance, information becomes rapidly out of date and mobility requires information about the environment to be discovered.
The ultimate goal of the research action is to provide a solid scientific foundation for the design of such systems, and to lay the groundwork for achieving effective principles for building and analysing such systems.
This workshop covers the aspects related to languages and programming environments as well as analysis of systems and resources involving 9 projects (AGILE , DART, DEGAS , MIKADO, MRG, MYTHS, PEPITO, PROFUNDIS, SECURE) out of the 13 founded under the initiative. After an year from the start of the projects, the goal of the workshop is to fix the state of the art on the topics covered by the two clusters related to programming environments and analysis of systems as well as to devise strategies and new ideas to profitably continue the research effort towards the overall objective of the initiative.
We acknowledge the Dipartimento di Informatica and Tlc of the University of Trento, the Comune di Rovereto, the project DEGAS for partially funding the event and the Events and Meetings Office of the University of Trento for the valuable collaboration
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
P Systems: from Anti-Matter to Anti-Rules
The concept of a matter object being annihilated when meeting its corresponding
anti-matter object is taken over for rule labels as objects and anti-rule labels
as the corresponding annihilation counterpart in P systems. In the presence of a corresponding
anti-rule object, annihilation of a rule object happens before the rule that the
rule object represents, can be applied. Applying a rule consumes the corresponding rule
object, but may also produce new rule objects as well as anti-rule objects, too. Computational
completeness in this setting then can be obtained in a one-membrane P system
with non-cooperative rules and rule / anti-rule annihilation rules when using one of the
standard maximally parallel derivation modes as well as any of the maximally parallel
set derivation modes (i.e., non-extendable (multi)sets of rules, (multi)sets with maximal
number of rules, (multi)sets of rules a ecting the maximal number of objects). When
using the sequential derivation mode, at least the computational power of partially blind
register machines is obtained
Setting Parameters for Biological Models With ANIMO
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for
modeling biological networks, such as e.g. signaling, metabolic or gene
networks. An ANIMO model is essentially the sum of a network topology and a
number of interaction parameters. The topology describes the interactions
between biological entities in form of a graph, while the parameters determine
the speed of occurrence of such interactions. When a mismatch is observed
between the behavior of an ANIMO model and experimental data, we want to update
the model so that it explains the new data. In general, the topology of a model
can be expanded with new (known or hypothetical) nodes, and enables it to match
experimental data. However, the unrestrained addition of new parts to a model
causes two problems: models can become too complex too fast, to the point of
being intractable, and too many parts marked as "hypothetical" or "not known"
make a model unrealistic. Even if changing the topology is normally the easier
task, these problems push us to try a better parameter fit as a first step, and
resort to modifying the model topology only as a last resource. In this paper
we show the support added in ANIMO to ease the task of expanding the knowledge
on biological networks, concentrating in particular on the parameter settings
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