Multiaction learning automata which update their action probabilities on the basis of the responses they get from an environment are considered in this paper. The automata update the probabilities according to whether the environment responds with a reward or a penalty. Learning automata are said to possess ergodicity of the mean if the mean action probability is the state probability (or uncon...

In this paper, we introduce a novel framework of cellular automata based computing that is capable of long short-term memory. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution...

We consider the problem of learning a finite automaton with recurrent neural networks, given a training set of sentences in a language. We train Elman recurrent neural networks on the prediction task and study experimentally what these networks learn. We found that the network tends to encode an approximation of the minimum automaton that accepts only the sentences in the training set.

The aim of this paper is the study of a covering of a max-mingeneral fuzzy automaton by another, admissible relations, admissiblepartitions of a max-min general fuzzy automaton,$tilde{delta}$-orthogonality of admissible partitions, irreduciblemax-min general fuzzy automata. Then we obtain the relationshipsbetween them.  

In formal language theory, we are mainly interested in the natural language computational aspects of ω-languages. Therefore in this respect it is convenient to consider fuzzy ω-languages. In this paper, we introduce two subclasses of fuzzy regular ω-languages called fuzzy n-local ω-languages and Buchi fuzzy n-local ω-languages, and give some closure properties for those subclasses. We define a ...

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. This response may be favorable or unfavorable. The aim is to design an automaton that can determine the best action guided by past actions and responses. The reinforce...

Recent studies have suggested the applicability of learning to automated compositional verification. However, current learning algorithms fall short when it comes to learning liveness properties. We extend the automaton synthesis paradigm for the infinitary languages by presenting an algorithm to learn an arbitrary regular set of infinite sequences (an ω-regular language) over an alphabet Σ. Ou...

This paper addresses the problem of learning a statistical distribution of data in a relational database. Data we want to focus on are represented with trees which are a quite natural way to represent structured information. These trees are used afterwards to infer a stochastic tree automaton, using a well-known grammatical inference algorithm. We propose two extensions of this algorithm: use o...

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using a reinforcement scheme based on the computational model of wasp behaviour that can determine the best action guided by past ac...

In this paper we introduce a finite automaton called partial finite automaton to recognize partial languages. We have defined three classes of partial languages, viz., local partial languages, regular partial languages and partial line languages. We present an algorithm for learning local partial languages in the limit from positive data. The time taken for the algorithm to identify the regular...