Automata learning is a technique that has successfully been applied in verification, with the automaton type varying depending on the application domain. Adaptations of automata learning algorithms for increasingly complex types of automata have to be developed from scratch because there was no abstract theory offering guidelines. This makes it hard to devise such algorithms, and it obscures th...

One of the critical challenges in wireless sensor networks (WSNs) is energy conservation. Topology control is a vital process to minimize energy consumption and maximize the network lifetime. In this paper, we propose a distributed topology protocol with transmission power adjustment based on harmony search and learning automata algorithms called HSLATC (Harmony Search and Learning Automata bas...

Minimum Steiner tree problem in a weighted graph is to find a least cost sub tree in the graph such that it contains special vertices called terminal points. Since this problem belongs to NP-Complete problems, many approximate algorithms including genetic algorithms and ant colony strategies have been designed for solving it. In this paper, we propose an algorithm based on distributed learning ...

This paper proposes a flexible transfer system (FTS) as one of the self-organizing manufacturing systems. The FTS is composed of autonomous robotic modules, which transfer a palette carrying an object. Through the selforganization of a multi-layered strategic vector field corresponding to a task, the FTS can generate quasioptimal transfer path in fully distributed way. We apply the learning aut...

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due ...

Because of unpredictable, uncertain and time-varying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, every feature of the graph such as path, clique, spanning tree and dominating set, to mention a few...

Symbolic automata allow transitions to carry predicates over rich alphabet theories, such as linear arithmetic, and therefore extend classic automata to operate over infinite alphabets, such as the set of rational numbers. In this paper, we study the foundational problem of learning symbolic automata. We first present Λ∗, a symbolic automata extension of Angluin’s L∗ algorithm for learning regu...

Weighted automata (WFAs) provide a general framework for the representation of functions mapping strings to real numbers. They include as special instances deterministic finite automata (DFAs), hidden Markov models (HMMs), and predictive states representations (PSRs). In recent years, there has been a renewed interest in weighted automata in machine learning due to the development of efficient ...