Using the convex combination of random binary variables, stochastic vectors and stochastic matrices are systematically realized. The techniques are extended to the realization of stochastic automata as randomly fed deterministic automata. Conditions for stochastic state equivalence are analyzed to uncover the connection with deterministic state equivalence. State minimality of both the stochast...

The paper proposes a simple formalism for dealing with deterministic, non-deterministic and stochastic cellular automata in a unifying and composable manner. Armed with this formalism, we extend the notion of intrinsic simulation between deterministic cellular automata, to the non-deterministic and stochastic settings. We then provide explicit tools to prove or disprove the existence of such a ...

The paper proposes a simple formalism for dealing with deterministic, non-deterministic and stochastic cellular automata in a unifying and composable manner. Armed with this formalism, we extend the notion of intrinsic simulation between deterministic cellular automata, to the non-deterministic and stochastic settings. We then provide explicit tools to prove or disprove the existence of such a ...

Reinforcement schemes represent the basis of the learning process for stochastic learning automata, generating their learning behavior. An automaton using a reinforcement scheme can decide the best action, based on past actions and environment responses. The aim of this paper is to introduce a new reinforcement scheme for stochastic learning automata. We test our schema and compare with other n...

Constraint automata have been used as an operational model for component connectors that coordinate the cooperation and communication of the components by means of a network of channels. In this paper, we introduce a variant of constraint automata (called continuous-time constraint automata) that allows us to specify time-dependent stochastic assumptions about the channel connections or the com...

This paper introduces (pronounce spades), a stochastic process algebra for discrete-event systems, that extends traditional process algebra with timed actions whose delay is governed by general (a.o. continuous) probability distributions. The operational semantics is defined in terms of stochastic automata, a model that uses clocks—like in timed automata—to symbolically represent randomly timed...

We model chemical and biochemical systems as collectives of interacting stochastic automata, with each automaton representing a molecule that undergoes state transitions. In this artificial biochemistry, automata interact by the equivalent of the law of mass action. We investigate several simple but intriguing automata collectives by stochastic simulation and by ODE analysis.