Memristors are novel non volatile devices that manage to combine storing and processing capabilities in the same physical place.Their nanoscale dimensions low power consumption enable further design of various nanoelectronic circuits corresponding computing architectures, like neuromorhpic, memory, unconventional, etc.One possible ways exploit memristor's advantages is by combining them with Ce...

Cognitive networking deals with using cognition to the entire network protocol stack to achieve stackwide, 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. Adding cognition to the existing Wireless Sensor Networks (WSNs) with a cognitive networking approach brings about many benefits....

Using finite-state automata for the text analysis component in a text-to-speech system is problematic in several respects: the rewrite rules from which the automata are compiled are difficult to write and maintain, and the resulting automata can become very large and therefore inefficient. Converting the knowledge represented explicitly in rewrite rules into a more efficient format is difficult...

Minimum vertex cover problem is an NP-Hard problem with the aim of finding minimum number of vertices to cover graph. In this paper, a learning automaton based algorithm is proposed to find minimum vertex cover in graph. In the proposed algorithm, each vertex of graph is equipped with a learning automaton that has two actions in the candidate or noncandidate of the corresponding vertex cover se...

The problem of analyzing the finite time behavior of learning automata is considered. This problem involves the finite time analysis of the learning algorithm used by the learning automaton and is important in determining the rate of convergence of the automaton. In this paper, a general framework for analyzing the finite time behavior of the automaton learning algorithms is proposed. Using thi...

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learni...

Learning algorithms are increasingly used in multiple disciplines of today's computer science reaching from robotics and formal verification to bioinformatics or natural language recognition. In this talk, we consider learning algorithms in the context of software development and verification. Two novel variants of learning algorithms will be presented. In the first part, we introduce NL* an al...

One of most attractive topics in grammatical inference is theoretically study on learnability of some classes of automata corresponding with defined formal languages. For last two decades a number of theoretical results have been reported and played as essential knowledge for applications in other fileds such as speech recognition and music-style recognition. In this paper, we consider the prob...

Markov games, as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi-agent systems. In this paper, several learning automata based multi-agent system algorithms for finding optimal policies in fully-cooperative Markov Games are proposed. In the proposed algorithms, Markov problem is described as a directed graph in which the nodes are ...