Reversible computing is a paradigm of computation that extends the standard forward-only programming to reversible programming, so that programs can be executed both in the standard, forward direction, and backward, going back to past states. In this paper we present novel quantitative stochastic models for concurrent and cooperating reversible computations. More precisely, we introduce the cla...

This paper demonstrates that random phenomena, although most often treated in the context of system malfunction, can play major constructive roles in the philosophy, theory and application of cybernetic systems. A control-theoretic example is given to show that a simple stochastic automaton can solve a regulator problem otherwise requiring a recursive automaton, and insoluble for finite-state a...

Stochastic Automata Networks (SANs) are an eecient means to describe and analyze parallel systems under Markovian assumptions. The main advantage of SANs is the possibility to describe and analyze a complex parallel system in a compositional way such that the transition matrix of the Markov chain underlying the complete SAN can be described in a compositional way using only small matrices speci...

Modest is a high-level compositional modelling language for stochastic timed systems with a formal semantics in terms of stochastic timed automata, an overarching formalism of which several well-studied models are special cases. The emphasis of Modest is to make use of existing analysis techniques and tools in a single-formalism, multiplesolution approach. In this paper, we focus on networks of...

In this paper, the concepts of somewhat fuzzy automata continuous functions and somewhat fuzzy automata open functions in fuzzy automata topological spaces are introduced and some interesting properties of these functions are studied. In this connection, the concepts of fuzzy automata resolvable spaces and fuzzy automata irresolvable spaces are also introduced and their properties are studied.

Markov chains are a well-known stochastic process that provide a balance between being able to adequately model the system’s behavior and being able to afford the cost of the model solution. Systems can be modeled directly as Markov chains, or with a higher-level formalism for which Markov chains represent the underlying semantics. Markov chains are widely used to study the performance of compu...

Small Generalised Stochastic Automata Bruce Litow and Olivier de Vel1 Abstract It is known, chie y through the work of Culik and Kari that generalised stochastic automata (GSA) can be used to compress digital (pixel) images. A theoretical account of GSA-based image compression has not been carried out. This paper contributes to such an account by exhibiting a family of images such that a member...

An SIA matrix is a stochastic matrix whose sequence of powers converges to a rank-one matrix. This convergence is desirable in various applications making use of stochastic matrices, such as consensus, distributed optimization and Markov chains. We study the shortest SIA products of sets of matrices. We observe that the shortest SIA product of a set of matrices is usually very short and we prov...

In this paper we consider some numerical issues in computing solutions to networks of stochastic automata (SAN). In particular our concern is with keeping the amount of computation per iteration to a minimum, since iterative methods appear to be the most eeective in determining numerical solutions. In a previous paper we presented complexity results concerning the vector-descriptor multiplicati...

In this paper, we introduce a Monte Carlo simulation method based on distributed learning automata for solving the stochastic shortest path problem. We give an iterative stochastic algorithm that find the minimum expected value of set of random variables representing cost of paths in a stochastic graph by taking sufficient samples from them. In the given algorithm, the sample size is determined...