The paper presents several illustrations of concept of subsystem when applying various types of mappings. There are specifications of notions as input-output system in a certain period of time, stage of system at a given moment, characterization of deterministic, nondeterministic and stochastic system and reasoning over their properties. In the end there are shown some applications of presented...

Cellular automata are usually updated synchronously and thus deterministically. The question of stochastic dynamics arises in the development of cellular automata resistant to noise [1] and in simulation of real life systems [2]. Synchronous updates may not be a valid hypothesis for such simulations and most of these studies use stochastic versions of cellular automata. In [3–6], the authors st...

We investigate the computational capabilities of probabilistic cellular automata by means of the density classification problem. We find that a specific probabilistic cellular automata rule is able to solve the density classification problem, i.e. classifies binary input strings according to the number of 1’s and 0’s in the string, and show that its computational abilities are related to critic...

In this paper we present a formalism based on stochastic automata to describe the stochastic dynamics of signal transduction networks that are specified by rule-sets. Our formalism gives a modular description of the underlying stochastic process, in the sense that it is a composition of smaller units – agent-views. The view of an agent is an automaton that identifies all local modification chan...

This paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable structures are a reinforcement learning method. Having no information about optimized operation, such models try to find an an...

Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of these finite state machines. In the setting of identification in the limit with probability one, we prove that stochastic deterministic finite automata cannot be identified from only a polynomial quantity of data. If concerned wit...

We consider reachability objectives on an extension of stochastic timed automata (STA) with nondeterminism. Decision stochastic timed automata (DSTA) are Markov decision processes based on timed automata where delays are chosen randomly and choices between enabled edges are nondeterministic. Given a reachability objective, the value 1 problem asks whether a target can be reached with probabilit...