Stochastic automata operating in an unknown random can be considered to show learning behavior. Tsypkin environment have been proposed earlier as models of learning. These [GT1] has recently argued that seemingly diverse problems automata update their action probabilities in accordance with the inputs . . . received from the environment and can improve their own performance inpa t rec i o idenf...

Abstract. Automata-based model checking is a widely used approach towards software model checking. Traditionally, nondeterministic Büchi automata are used to represent the temporal logic property to be checked. We take a look at a special kind of alternating automata, the linear weak alternating automata. They can be constructed from LTL formula in an elegant way in linear time. The emptiness c...

The semantic automata framework, developed originally in the 1980s, provides computational interpretations of generalized quantifiers. While recent experimental results have associated structural features of these automata with neuroanatomical demands in processing sentences with quantifiers, the theoretical framework has remained largely unexplored. In this paper, after presenting some classic...

anti-lock braking system (abs) which is a nonlinear and time variant system may not be easily controlled by classic control methods. this is due to the fact that classic linear controllers are just capable of controlling a specific plant in small region of state space. to overcome this problem, a more powerful control technique must be employed for complex nonlinear plants. fuzzy controllers ar...

image segmentation is an important task in image processing and computer vision which attract many researchers attention. there are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. markov random field (mrf) is a tool for modeling statistical and structural inf...

Learning Automata update their action probabilites on the basis of the response they get from a random environment. They use a reward adaptation rate for a favorable environment's response and a penalty adaptation rate for an unfavorable environment's response. In this correspondence, we introduce Multiple Response learning automata by explicitly classifying the environment responses into a rew...

In this paper, we present an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow. Register automata operate on an infinite data domain, whose values can be assigned to registers and compared for equality. Our active learning algorithm is unique in that it directly infers the effect of data values on ...

multilayer bach propagation neural networks have been considered by researchers. despite their outstanding success in managing contact between input and output, they have had several drawbacks. for example the time needed for the training of these neural networks is long, and some times not to be teachable. the reason for this long time of teaching is due to the selection unsuitable network par...

We present spectral methods of moments for learning sequential models from a single trajectory, in stark contrast with the classical literature that assumes the availability of multiple i.i.d. trajectories. Our approach leverages an efficient SVD-based learning algorithm for weighted automata and provides the first rigorous analysis for learning many important models using dependent data. We st...