Abstract AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in learning, as well intuitive seamlessly integrated interface characterizing real-world reactive In this article, we present ’s core functional...

In the past decade, active automata learning, an originally merely theoretical enterprise, got attention as a method for dealing with black-box or third party systems. Applications ranged from the support of formal verification, e.g. for assume guarantee reasoning [4], to usage of learned models as the basis for regression testing. In the meantime, a number of approaches exploiting active learn...

One of the basic restrictions in Ad Hoc Wireless Networks is energy supply and because of that proposing of power saving protocols that do the normal tasks of network without significantly diminishing the quality of services of the network and consequently, prolonging the lifetime of network has high importance. So, in this paper a distributed power saving technique for multi-hop ad hoc wireles...

In this paper, a new evolutionary computing model, called CLA-EC, is proposed. This model is a combination of a model called cellular learning automata (CLA) and the evolutionary model. In this model, every genome in the population is assigned to one cell of CLA and each cell in CLA is equipped with a set of learning automata. Actions selected by learning automata of a cell determine the genome...

We have trained networks of E II units with short-range connections to simulate simple cellular automata that exhibit complex or chaotic behaviour. Three levels of learning are possible (in decreasing order of difficulty): learning the underlying automaton rule, learning asymptotic dynamical behaviour, and learning to extrapolate the training history. The levels of learning achieved with and wi...

Adding cognition to the existing Wireless Sensor Networks (WSNs) with a cognitive networking approach brings about many benefits. Cognitive networking deals with using cognition to the entire network protocol stack to achieve stack-wide as well as network-wide performance goals; unlike cognitive radios that apply cognition only at the physical layer to overcome the problem of spectrum scarcity....

In this paper, the concepts of somewhat fuzzy automata continuous functions and somewhat fuzzy automata open functions in fuzzy automata topological spaces are introduced and some interesting properties of these functions are studied. In this connection, the concepts of fuzzy automata resolvable spaces and fuzzy automata irresolvable spaces are also introduced and their properties are studied.

Learning automata have been found to be useful in the systems with incomplete knowledge. Therefore, it can be used as a tool to solve problems of Ad Hoc networks, where nodes aremobile and operatewithin a dynamic environment, which entails possibly unknown and time varying characteristics. In this paper, after a short review on the related works, learning automata and CEC algorithm, which is a ...

In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem. These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length). In these algorithms, at...