ly, a learning automaton [2] can be considered to be an object that can choose from a finite number of actions. For every action that it chooses, the random environment in which it operates evaluates that action. A corresponding feedback is sent to the automaton based on which the next action is chosen. As this process progresses the automaton learns to choose the optimal action for that unknow...

In this paper, we define the notion of a bipolar general fuzzy automaton, then we construct some closure operators on the set of states of a bipolar general fuzzy automaton. Also, we construct some topologies on the set of states of a bipolar general fuzzy automaton. Then we obtain some relationships between them.

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 an evolutionary reinforcement scheme (the basis of the learning process), that can determine the best action guided by past ac...

the purpose of the present work is to introduce the concept of bifuzzy core of a fuzzy automaton, which induces a bifuzzy topology on the state-set of this fuzzy automaton. this is shown that this bifuzzy topology can be used to characterize the concepts such as bifuzzy family of submachines, bifuzzy separable family and bifuzzy retrievable family of a fuzzy automaton.

In this paper, we study an adaptive random search method based on continuous action-set learning automaton for solving stochastic optimization problems in which only the noisecorrupted value of function at any chosen point in the parameter space is available. We first introduce a new continuous action-set learning automaton (CALA) and study its convergence properties. Then we give an algorithm ...

For a learning automaton, a proper configuration of its learning parameters, which are crucial for the automaton’s performance, is relatively difficult due to the necessity of a manual parameter tuning before real applications. To ensure a stable and reliable performance in stochastic environments, parameter tuning can be a time-consuming and interactioncosting procedure in the field of LA. Esp...

Information extraction (IE) aims at extracting specific information from a collection of documents. A lot of previous work on IE from semi-structured documents (in XML or HTML) uses learning techniques based on strings. Some recent work converts the document to a ranked tree and uses tree automaton induction. This paper introduces an algorithm that uses unranked trees to induce an automaton. Ex...

A MAT learning algorithm is presented that infers the universal automaton for a regular target language, using a polynomial number of queries with respect to that automaton. The universal automaton is one of several canonical characterizations for regular languages Our learner is based on the concept of an observation table, which seems to be particularly fitting for this computational model, a...