In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language. The estimate of P stands in some class of probabilistic models such as probabilistic automata (PA). In this paper, we focus on probabilistic models based on multiplicity automata (MA). The stochastic languages generated by MA are called rational stocha...

Given a finite set of words w1, . . . , wn independently drawn according to a fixed unknown distribution law P called a stochastic language, an usual goal in Grammatical Inference is to infer an estimate of P in some class of probabilistic models, such as Probabilistic Automata (PA). Here, we study the class S R (Σ) of rational stochastic languages, which consists in stochastic languages that c...

Supervisory control and fault diagnosis of hybrid systems need to have complete information about the discrete states transitions of the underling system. From this point of view, the hybrid system should be abstracted to a Discrete Trace Transition System (DTTS) and represented by a discrete mode transition graph. In this paper an effective method is proposed for generating discrete mode trans...

Finding a ground state of given Hamiltonian an Ising model on graph $$G=(V,E)$$ is important but hard problem. The standard approach for this kind problem the application algorithms that rely single-spin-flip Markov chain Monte Carlo methods, such as simulated annealing based Glauber or Metropolis dynamics. In paper, we investigate particular stochastic cellular automata, in which all spins are...

Learning automata act in a stochastic environment and are able to update their action probabilities considering the inputs from their environment, so optimizing their functionality as a result. In this paper, the goal is to investigate and evaluate the application of learning automata to cooperation in multiagent systems, using soccer simulation server as a test bed. We have also evaluated our ...