2,474 research outputs found
On the decomposition of stochastic cellular automata
In this paper we present two interesting properties of stochastic cellular
automata that can be helpful in analyzing the dynamical behavior of such
automata. The first property allows for calculating cell-wise probability
distributions over the state set of a stochastic cellular automaton, i.e.
images that show the average state of each cell during the evolution of the
stochastic cellular automaton. The second property shows that stochastic
cellular automata are equivalent to so-called stochastic mixtures of
deterministic cellular automata. Based on this property, any stochastic
cellular automaton can be decomposed into a set of deterministic cellular
automata, each of which contributes to the behavior of the stochastic cellular
automaton.Comment: Submitted to Journal of Computation Science, Special Issue on
Cellular Automata Application
ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
Background: Many biological systems are modeled qualitatively with discrete
models, such as probabilistic Boolean networks, logical models, Petri nets, and
agent-based models, with the goal to gain a better understanding of the system.
The computational complexity to analyze the complete dynamics of these models
grows exponentially in the number of variables, which impedes working with
complex models. Although there exist sophisticated algorithms to determine the
dynamics of discrete models, their implementations usually require
labor-intensive formatting of the model formulation, and they are oftentimes
not accessible to users without programming skills. Efficient analysis methods
are needed that are accessible to modelers and easy to use. Method: By
converting discrete models into algebraic models, tools from computational
algebra can be used to analyze their dynamics. Specifically, we propose a
method to identify attractors of a discrete model that is equivalent to solving
a system of polynomial equations, a long-studied problem in computer algebra.
Results: A method for efficiently identifying attractors, and the web-based
tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other
analysis methods for discrete models. ADAM converts several discrete model
types automatically into polynomial dynamical systems and analyzes their
dynamics using tools from computer algebra. Based on extensive experimentation
with both discrete models arising in systems biology and randomly generated
networks, we found that the algebraic algorithms presented in this manuscript
are fast for systems with the structure maintained by most biological systems,
namely sparseness, i.e., while the number of nodes in a biological network may
be quite large, each node is affected only by a small number of other nodes,
and robustness, i.e., small number of attractors
A Component-oriented Framework for Autonomous Agents
The design of a complex system warrants a compositional methodology, i.e.,
composing simple components to obtain a larger system that exhibits their
collective behavior in a meaningful way. We propose an automaton-based paradigm
for compositional design of such systems where an action is accompanied by one
or more preferences. At run-time, these preferences provide a natural fallback
mechanism for the component, while at design-time they can be used to reason
about the behavior of the component in an uncertain physical world. Using
structures that tell us how to compose preferences and actions, we can compose
formal representations of individual components or agents to obtain a
representation of the composed system. We extend Linear Temporal Logic with two
unary connectives that reflect the compositional structure of the actions, and
show how it can be used to diagnose undesired behavior by tracing the
falsification of a specification back to one or more culpable components
Reo + mCRL2: A Framework for Model-checking Dataflow in Service Compositions
The paradigm of service-oriented computing revolutionized the field of software engineering. According to this paradigm, new systems are composed of existing stand-alone services to support complex cross-organizational business processes. Correct communication of these services is not possible without a proper coordination mechanism. The Reo coordination language is a channel-based modeling language that introduces various types of channels and their composition rules. By composing Reo channels, one can specify Reo connectors that realize arbitrary complex behavioral protocols. Several formalisms have been introduced to give semantics to Reo. In their most basic form, they reflect service synchronization and dataflow constraints imposed by connectors. To ensure that the composed system behaves as intended, we need a wide range of automated verification tools to assist service composition designers. In this paper, we present our framework for the verification of Reo using the toolset. We unify our previous work on mapping various semantic models for Reo, namely, constraint automata, timed constraint automata, coloring semantics and the newly developed action constraint automata, to the process algebraic specification language of , address the correctness of this mapping, discuss tool support, and present a detailed example that illustrates the use of Reo empowered with for the analysis of dataflow in service-based process models
Reo + mCRL2: A Framework for Model-Checking Dataflow in Service Compositions
The paradigm of service-oriented computing revolutionized the field of software
engineering. According to this paradigm, new systems are composed of existing
stand-alone services to support complex cross-organizational business
processes. Correct communication of these services is not possible without a
proper coordination mechanism. The Reo coordination language is a channel-based
modeling language that introduces various types of channels and their
composition rules. By composing Reo channels, one can specify Reo connectors
that realize arbitrary complex behavioral protocols. Several formalisms have
been introduced to give semantics to Reo. In their most basic form, they
reflect service synchronization and dataflow constraints imposed by connectors.
To ensure that the composed system behaves as intended, we need a wide range of
automated verification tools to assist service composition designers. In this
paper, we present our framework for the verification of Reo using the mCRL2
toolset. We unify our previous work on mapping various semantic models for Reo,
namely, constraint automata, timed constraint automata, coloring semantics and
the newly developed action constraint automata, to the process algebraic
specification language of mCRL2, address the correctness of this mapping,
discuss tool support, and present a detailed example that illustrates the use
of Reo empowered with mCRL2 for the analysis of dataflow in service-based
process models
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