Sharif Digital Repository

The cellular learning automaton (CLA), which is a combintion of cellular automaton (CA) and learning automaton (LA), is introduced recently. This model is superior to CA because of its ability to learn and is also superior to single LA because it is a collection of LAs which can interact with each other. The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. Recently, various types of CLA such as synchronous, asynchronous, and open CLAs have been introduced. In some applications such as cellular networks, we need to have a model of CLA for which multiple LAs reside in each cell. In this paper, we study a CLA model for which each cell has several LAs.... 

Due to unstoppable growth of social networks and the large number of users, the detection of communities have become one of the most popular and successful domain of research areas. Detecting communities is a significant aspect in analyzing networks because of its various applications such as sampling, link prediction and communications among members of social networks. There have been proposed many different algorithms for solving community detection problem containing optimization methods. In this paper we propose a novel algorithm based on extended distributed learning automata for solving this problem. Our proposed algorithm benefits from cooperation between learning automata to detect... 

This paper studies an iterative stochastic algorithm for solving the stochastic shortest path problem. This algorithm, which uses a distributed learning automata, tries to find the shortest path by taking a sufficient number of samples from the edges of the graph. In this algorithm, which edges to be sampled are determined dynamically as the algorithm proceeds. At each iteration of this algorithm, a distributed learning automata used to determine which edges to be sampled. This sampling method, which uses distributed learning automata, reduces the number of samplings from those edges, which may not be along the shortest path, and resulting in a reduction in the number of the edges to be... 

Maximum independent set problem is an NP-Hard one with the aim of finding the set of independent vertices with maximum possible cardinality in a graph. In this paper, we investigate a learning automaton based algorithm that finds a maximum independent set in the graph. Initially, a learning automaton is assigned to each vertex of graph. In order to find candidate independent set, a set of distributed learning automata collaborate with each other. The proposed algorithm based on learning automata is guided iteratively to the maximum independent set by updating the action probability vector. In order to study the performance of the proposed algorithm, we conducted some experiments. The... 

In this paper, we study the problem of finding the shortest path in stochastic graphs and propose an iterative algorithm for solving it. This algorithm is based on distributed learning automata (DLA), and its objective is to use a DLA for finding the shortest path from the given source node to the given destination node whose weight is minimal in expected sense. At each stage of this algorithm, DLA specifies edges needed to be sampled. We show that the given algorithm finds the shortest path with minimum expected weight in stochastic graphs with high probability which can be close to unity as much as possible. We compare the given algorithm with some distributed learning automata-based... 

In this paper, we propose a novel algorithm that combines color and texture information of skin with cellular learning automata to segment skin-like regions in color images. First, the presence of skin colors in an image is detected, using a committee structure, to make decision from several explicit boundary skin models. Detected skin-color regions are then fed to a color texture extractor that extracts the texture features of skin regions via their color statistical properties and maps them to a skin probability map. Cellular learning automatons use this map to make decision on skin-like regions. The proposed algorithm has demonstrated true positive rate of about 83.4% and false positive... 

Soccer model and relation of players and coach has been analyzed by a learning automata-based method, called Soccer Mobility Estimator (SME), who estimates the mobility model of soccer players. During a soccer match, players play according to a certain program designed by coach. The pattern of players' mobility is not stochastic and it can be assumed that they are playing with a certain mobility model. Since knowledge about mobility model of nodes in mobile ad-hoc networks has a substantial effect on its performance evaluation, knowledge about mobility model of soccer players can be useful for coaches and experts for game analysis. In fact the mobility model of players could be an important...