Sharif Digital Repository

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 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... 

Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the action of each cell in the next stage of its evolution not only depends on the local environment (actions of its neighbors) but it also depends on the external environments. We call such a cellular learning automata as open cellular learning automata. In this paper, we introduce open cellular learning... 

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is... 

Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in different cells are activated synchronously, has found many applications. In some applications a type of cellular learning automata in which learning automata in different cells are activated asynchronously (asynchronous cellular learning automata) is needed. In this paper, we introduce asynchronous cellular learning automata and study its steady state behavior. Then an application of this new model to cellular networks has been presented. © 2008  

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 each stage distributed learning automata determines which edges to be sampled. This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms. It is shown that the shortest path is found with a probability as close as to unity by proper choice of... 

Cellular learning automata (CLA) which has been introduced recently, is a combination of cellular automata (CA) and learning automata (LA). A CLA is a CA in which a LA is assigned to its every cell. The LA residing in each cell determines the state of the cell on basis of its action probability vector. Like CA, there is a local rule that CLA operates under it. In this paper we introduce a new model of CLA in which each cell gets an external input vector from the environment in addition to reinforcement signal, so this model can work in non-stationary environments. Then two applications of the new model on image segmentation and clustering are given, and the results show that the proposed... 

Hierarchical reinforcement learning facilitates learning in large and complex domains by exploiting subtasks and creating hierarchical structures using these subtasks. Subtasks are usually defined through finding subgoals of the problem. Providing mechanisms for autonomous subgoal discovery and skill acquisition is a challenging issue in reinforcement learning. Among the proposed algorithms, a few of them are successful both in performance and also efficiency in terms of the running time of the algorithm. In this paper, we study four methods for subgoal discovery which are based on graph partitioning. The idea behind the methods proposed in this paper is that if we partition the transition... 

In this paper, an adaptive random search method, based on continuous action-set learning automata, is studied for solving stochastic optimization problems in which only the noisecorrupted value of a function at any chosen point in the parameter space is available. First, a new continuous action-set learning automaton is introduced and its convergence properties are studied. Then, applications of this new continuous action-set learning automata to the minimization of a penalized Shubert function and pattern classification are presented. © Sharif University of Technology  

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... 

A solution to channel assignment problem in cellular networks is self-organizing channel assignment algorithm with distributed control. In this paper, we propose three cellular learning automata based dynamic channel assignment algorithms. In the first two algorithms, no information about the status of channels in the whole network will be used by cells for channel assignment whereas in the third algorithm, the additional information regarding status of channels may be gathered and then used by cells in order to allocate channels. The simulation results show that by using the proposed channel assignment algorithms the micro-cellular network can self-organize itself. The simulation results... 

In this paper, we first propose two learning automata based decentralized dynamic guard channel algorithms for cellular mobile networks. These algorithms use learning automata to adjust the number of guard channels to be assigned to cells of network. Then, we introduce a new model for nonstationary environments under which the proposed algorithms work and study their steady state behavior when they use LR-I learning algorithm. It is also shown that a learning automaton operating under the proposed nonstationary environment equalizes its penalty strengths. Computer simulations have been conducted to show the effectiveness of the proposed algorithms. The simulation results show that the... 

In this paper, a new fixed structure learning automaton (FSSA), with a tuning parameter for amount of its rewards, is presented and its behavior in stationary environments will be studied. This new automaton is called TFSLA (Tunable Fixed Structured Learning Automata). The proposed automaton characterizes by star shaped transition diagram and each branch of the star contains N states associated with a particular action. TFSLA is tunable, so that the automaton can receive reward flexibly, even when it accepted penalty according to its previous action. Experiments show that TFSLA converges to the optimal action faster than some older FSSAs (e.g. Krinsky and Krylov) and the analytic examination... 

In this paper, we present a new fixed structure learning automaton (FSSA), called IJA, and study its steady state behavior in stationary environments. The proposed automaton characterizes by star shaped transition diagram and each branch of the star contains N states associated with a particular action. This new automaton is an improvement of the Krinsky FSSA and not only ε-optimal and expedient, but also converges to the optimal action faster than the old one. © 2006 IEEE  

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.... 

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... 

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose an algorithm to solve the web page recommendation problem. In our algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned pattern. Our experiments on real data set show that the proposed algorithm performs better than the other algorithms that we compared to and, at the same time, it is... 

In this paper, two learning automata based adaptive limited fractional guard channel algorithms for cellular mobile networks are proposed. These algorithms try to minimize the blocking probability of new calls subject to the constraint on the dropping probability of the handoff calls. To evaluate the proposed algorithms, computer simulations are conducted. The simulation results show that the performance of the proposed algorithms are close to the performance of the limited fractional guard channel algorithm for which prior knowledge about traffic parameters are needed. The simulation results also show that the proposed algorithms outperforms the recently introduced dynamic guard channel... 

Cellular learning automata (CLA) is a distributed computational model which was introduced in the last decade. This model combines the computational power of the cellular automata with the learning power of the learning automata. Cellular learning automata is composed from a lattice of cells working together to accomplish their computational task; in which each cell is equipped with some learning automata. Wide range of applications utilizes CLA such as image processing, wireless networks, evolutionary computation and cellular networks. However, the only input to this model is a reinforcement signal and so it cannot receive another input such as the state of the environment. In this paper,...