This paper studies the problem of learning weighted automata from a finite sample of strings with real-valued labels. We consider several hypothesis classes of weighted automata defined in terms of three different measures: the norm of an automaton’s weights, the norm of the function computed by an automaton, and the norm of the corresponding Hankel matrix. We present new data-dependent general...

Introduction of micro-cellular networks offer a potential increase in capacity of cellular networks, but they create problems in management of the cellular networks. A solution to these problems is self-organizing channel assignment algorithm with distributed control. In this paper, ''le first introduce the model of cellular learning automata in which learning automata are used to adjust the st...

This paper presents libalf, a comprehensive, open-source library for learning formal languages. libalf covers various well-known learning techniques for finite automata (e.g. Angluin’s L∗, Biermann, RPNI etc.) as well as novel learning algorithms (such as for NFA and visibly one-counter automata). libalf is flexible and allows facilely interchanging learning algorithms and combining domain-spec...

We will demonstrate the impact of the integration of our most recently developed learning technology for inferring Register Automata into the LearnLib, our framework for active automata learning. This will not only illustrate the unique power of Register Automata, which allows one to faithfully model data independent systems, but also the ease of enhancing the LearnLib with new functionality.

This paper studies the problem of learning weighted automata from a finite labeled training sample. We consider several general families of weighted automata defined in terms of three different measures: the norm of an automaton’s weights, the norm of the function computed by an automaton, or the norm of the corresponding Hankel matrix. We present new data-dependent generalization guarantees fo...

In the last decades, a myriad of approaches to the multi-armed bandit problem have appeared in several different fields. The current top performing algorithms from the field of Learning Automata reside in the Pursuit family, while UCB-Tuned and the ε-greedy class of algorithms can be seen as state-ofthe-art regret minimizing algorithms. Recently, however, the Bayesian Learning Automaton (BLA) o...

anti-lock braking system (abs) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple type-1 fuzzy sets may not be robust enough to overcome uncertainties. for this reason an interval type-2 fuzzy controller is de...

In a recent work, Gandhi, Khoussainov, and Liu [7] introduced and studied a generalized model of finite automata able to work over arbitrary structures. As one relevant area of research for this model the authors identify studying such automata over partciular structures such as real and algebraically closed fields. In this paper we start investigations into this direction. We prove several str...

This paper presents a general approach to image segmentation and object recognition that learns a mapping from images with varying properties to segmentation algorithm parameters. The mapping is built using a reinforcement learning algorithm that is based on a team of generalized stochas-tic learning automata and operates separately in a global or local manner on an image. The edge-border coinc...

Inductive learning is the method of learning from observations. Inductive learning has important applications over a wide range of area including pattern recognition, language acquisition, bio-informatics and intelligent agent design. Because of such diverse applicability, inductive learning methods including automata learning, grammar induction, hidden markov model learning and symbolic statis...