In this note first we define a BCK‐algebra on the states of a deterministic finite automaton. Then we show that it is a BCK‐algebra with condition (S) and also it is a positive implicative BCK‐algebra. Then we find some quotient BCK‐algebras of it. After that we introduce a hyper BCK‐algebra on the set of all equivalence classes of an equivalence relation on the states of a deterministic finite...

Abstract We present algorithms for model checking and controller synthesis of timed automata, seeing a automaton as parallel composition large finite-state machine relatively smaller automaton, using compositional reasoning on this composition. use automata learning to learn finite approximations the component, in order reduce problem at hand or synthesis. an experimental evaluation our approach.

We consider the problem of learning a nite automaton with recurrent n e u r a l networks from positive evidence. We train Elman recurrent neural networks with a set of sentences in a language and extract a nite automaton by clustering the states of the trained network. We observe that the generalizations beyond the training set, in the language recognized by the extracted automaton, are due to ...

In this paper, we rst propose a new continuous action learning automaton which uses a stochastic estimator learning algorithm to increase the speed of convergence. Then we introduce an adaptive and autonomous call admission algorithm, which uses the proposed learning automaton. The proposed algorithm minimizes blocking probability of new calls subject to constraint on dropping probability of ha...

A learning automaton systematically updates a strategy to enhance the performance of a system output. The authors apply, a variable-structure learning automaton to achieve a best compromise solution between the economic operation and stable operation in a power system when the loads vary randomly. Both the generation cost for economic operation and the modal performance measure for stable opera...

In this paper, a new delay shift approach for learning in an RBF-like neural network structure of spiking neurons is introduced. The synaptic connections between the input and the RBF neurons are single delayed connections and the delays are adapted during an unsupervised learning process. Each synaptic connection in this network is modeled by a learning automaton. The action of the automaton a...

This paper describes two novel on-policy reinforcement learning algorithms, named QV(λ)-learning and the actor critic learning automaton (ACLA). Both algorithms learn a state value-function using TD(λ)-methods. The difference between the algorithms is that QV-learning uses the learned value function and a form of Q-learning to learn Q-values, whereas ACLA uses the value function and a learning ...

11 More work should be done on learning homomorphisms into larger alphabets. More importantly, it would be interesting to nd natural linguistic limitations of the type of morphological transformations such that the resultant learning problem would become tractable. References 1] Angluin, D. and C. Smith, \Inductive inference: theory and methods". Comput. QUESTION: Is there a K-state determinist...

We consider the complexity of equivalence and learning for multiplicity tree automata, i.e., weighted tree automata over a field. We first show that the equivalence problem is logspace equivalent to polynomial identity testing, the complexity of which is a longstanding open problem. Secondly, we derive lower bounds on the number of queries needed to learn multiplicity tree automata in Angluin’s...