Asynchronous automata are a model of communication processes with a distributed control structure, global initializations and global accepting conditions. The well-known theorem of Zielonka states that they recognize exactly the class of regular Mazurkiewicz trace languages. In this paper we study the particular case of distributed asynchronous automata, which require that the initializations a...

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...

An important problem in distributed systems that are subject to component failures is the distributed diagnosis problem. In distributed diagnosis, each working node must maintain correct information about the status (working or failed) of each component in the system. In this paper we consider the problem of identifying faulty (crashed) nodes in a wireless sensor network and used timed automata...

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...

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 this work we develop a temporal logic with fixpoints for distributed processes or distributed systems. Standard temporal logics refer to sequential processes (execution sequences) of distributed systems. Expressive temporal operators such as “Until” can be expressed as fixpoints of formulae involving only the simple “Next” modality. Fixpoint logics for sequential processes are known to be ex...

Optimized task scheduling is one of the most important challenges in multiprocessor environments such as parallel and distributed systems. In such these systems, each parallel program is decomposed into the smaller segments so-called tasks. Task execution times, precedence constrains and communication costs are modeled by using a directed acyclic graph (DAG) named task graph. The goal is to min...

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...