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An adaptive Petri net, called APN-LA, that has been recently introduced, uses a set of learning automata for controlling possible conflicts among the transitions in a Petri net (PN). Each learning automaton (LA) in APN-LA acts independently from the others, but there could be situations, where the operation of a LA affects the operation of another...
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Fifth-generation (5G) networks are already available in major urban areas and are expected to bring a major transformation to citizens’ lives. 5G services, such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (mMTC), require a network infrastructure capable of supportin...
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... Reciprocally, in irregular CLA methods such as [48], the CLA is modeled in the form of an indirect graph. Since the irregular type is favorable for a scattered component such as graph [49] or network [50] applications, the suggested TRCLA algorithm for NT avoidance uses the regular type. ...
In most traditional machine learning algorithms, the training and testing datasets have identical distributions and feature spaces. However, these assumptions have not held in many real applications. Although transfer learning methods have been invented to fill this gap, they introduce new challenges as negative transfers (NTs). Most previous research considered NT a significant problem, but they pay less attention to solving it. This study will propose a transductive learning algorithm based on cellular learning automata (CLA) to alleviate the NT issue. Two famous learning automata (LA) entitled estimators are applied as estimator CLA in the proposed algorithms. A couple of new decision criteria called merit and and attitude parameters are introduced to CLA to limit NT. The proposed algorithms are applied to standard LA environments. The experiments show that the proposed algorithm leads to higher accuracy and less NT results.
... The pioneering work of LA can be traced back to 1960s by a vanguard scholar Tsetlin [6,34]. By now, LA has generated immense potentials in applications such as social complex network [2,28,29,32], tutorial system [19,20], immune system [25,26], stochastic graph [4,5,11,15,27], image recognition [7,10,14], and Petri nets [8,35,36]. One can consult the book [16,30,31] for more various and detailed LA applications. ...
Learning automata (LA), a powerful tool for reinforcement learning in the field of machine learning, could explore its optimal state by continuously interacting with an external environment. Generally, the traditional LA algorithms, especially estimator LA algorithms, can be ultimately abstracted out as P- or Q-models, which are simply located in the stationary environments. A more comprehensive consideration would be S-model operating in the non-stationary environment. For this specific LA, presently the most popular achievement belongs to stochastic estimator LA (SELA). However, synchronously handing four parameters involved in SELA is an intractable job, as these parameters may vary dramatically in values under different environments, making it essential to develop a strategy for parameter tuning. In this paper, we first propose a scheme to determine the parameter searching scope and subsequently present a series of parameter searching methods, including a four-dimensional method and a two-dimensional method, making SELA applicable for any environment with switching non-stationary characteristics. Furthermore, to decrease the tuning cost, a reduced parameter SELA supported by the new two-dimensional parameter searching method emerges. And to break the traditional limit that the environmental reward probability must be symmetrically distributed, the S-model is constructed from a new perspective, thus forming a novel reduced parameter S-model of SELA (rpS-SELA). A detailed mathematical proof theoretically reveals the absolute expediency of rpS-SELA. In addition, it is demonstrated by experimental simulations that rpS-SELA outperforms others with a reduced tuning cost, a minor time consumption, a higher accuracy rate, and above all, a stronger tracking ability to the environmental switches.
... The local environment is called non-stationary because the action probability vectors in the neighboring learning automatons change during the evolution of the ICLA. This approach has been used in spatial analysis problems as sampling social networks [8] and vertex coloring problem [22]. From this approach, the theoretical bases for the development of proposal are established. ...
The stratification of territories is a powerful tool for the analysis and trend in health studies. An important element in health studies is the relationship established between geographical location and health indicators in correspondence with the first law of Geography. From this approach, the formation of compact strata allows the identification of local and global trends. This paper presents method for territories stratification in Geographic Information Systems. A clustering algorithm based on cellular automata theory is proposed to incorporate the treatment to heterogeneity and spatial dependence. The results obtained from the evaluation of validation indices demonstrates the utility and applicability of the proposal.
... Leaning automata have been successfully used in many applications such as image processing [35,36], combinatorial optimization problems [37][38][39], stochastic graphs [37,[40][41][42], wireless networks [43], social networks [44][45][46], network sampling [47], grid computing [48], cloud computing [49], and adaptive petri-net [50] to mention only a few. ...
In recent years, with the rapid development of online social networks, an enormous amount of information has been generated and diffused by human interactions through online social networks. The availability of information diffused by users of online social networks has facilitated the investigation of information diffusion and influence maximization. In this paper, we focus on the influence maximization problem in social networks, which refers to the identification of a small subset of target nodes for maximizing the spread of influence under a given diffusion model. We first propose a learning automaton-based algorithm for solving the minimum positive influence dominating set (MPIDS) problem, and then use the MPIDS for influence maximization in online social networks. We also prove that by proper choice of the parameters of the algorithm, the probability of finding the MPIDS can be made as close to unity as possible. Experimental simulations on real and synthetic networks confirm the superiority of the algorithm for finding the MPIDS Experimental results also show that finding initial target seeds for influence maximization using the MPIDS outperforms well-known existing algorithms.
... • Regular CLA vs. irregular CLA (ICLA): in a regular CLA, the structure of the CLA is represented as a lattice of d-tuples of integer numbers, while in an irregular CLA (ICLA), the structure regularity assumption is replaced with an undirected graph. [18][19][20] III. WAVEFRONT CLA A wavefront cellular learning automaton is a generalization of the asynchronous CLA which has a connected neighbor structure and the property of diffusion. ...
This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.
... Because of distributed adaptation capability of the CLAs, they have found application in computer networks [24][25][26][27]. These models have been also used in areas such as social networks [28], Petri nets [29], and evolutionary computing [30] to mention a few. Recently, dynamic models of CLAs (DCLAs) have been reported in [24,31], and [32]. ...
... According to the results of this experiment which are shown in Figs. 22,23,24,25,26,27,28,29,30,31, 32 and 33 one may conclude that X-NET algorithm performs better than other algorithms with respect to NSP and CU. It can be noted from the results, that as the time passes the performance of the proposed algorithm in terms of PTO, and CMO improves. ...
Super-peer networks refer to a class of peer-to-peer networks in which some peers called super-peers are in charge of managing the network. A group of super-peer selection algorithms use the capacity of the peers for the purpose of super-peer selection where the capacity of a peer is defined as a general concept that can be calculated by some properties, such as bandwidth and computational capabilities of that peer. One of the drawbacks of these algorithms is that they do not take into consideration the dynamic nature of peer-to-peer networks in the process of selecting super-peers. In this paper, an adaptive super-peer selection algorithm considering peers capacity based on an asynchronous dynamic cellular learning automaton has been proposed. The proposed cellular learning automaton uses the model of fungal growth as it happens in nature to adjust the attributes of the cells of the cellular learning automaton in order to take into consideration the dynamicity that exists in peer-to-peer networks in the process of super-peers selection. Several computer simulations have been conducted to compare the performance of the proposed super-peer selection algorithm with the performance of existing algorithms with respect to the number of super-peers, and capacity utilization. Simulation results have shown the superiority of the proposed super-peer selection algorithm over the existing algorithms.
... In an Irregular CLA, the structure regularity assumption is removed. This model has been used in solving problems which can be modeled by graphs with irregular structures such as [19][20][21][22] Weight representing the amount of flow from node i to node j. ...
Cellular learning automata (CLAs) are learning models that bring together the computational power of cellular automata and also the learning capability of learning automata in unknown environments. CLAs can be open or closed. In a closed CLA, the action of each learning automaton depends on the neighboring cells, whereas in an open CLA, the action of each learning automaton depends on the neighboring cells, and a global environment. These models can be synchronous or asynchronous. In a synchronous CLA, all cells are activated at the same time, but in an asynchronous CLA, at a given time only some cells are activated. These models can be also static or dynamic. In a dynamic CLA, one of its aspects such as structure, local rule or neighborhood may vary with time. All existing dynamic models of the CLAs are closed. In this paper, an open asynchronous dynamic CLA has been introduced. In order to show the potential of this model, an algorithm based on this model for solving allocation hub location problem with imprecise distances among nodes has been designed. To evaluate the proposed algorithm computer simulations have been conducted. The results of simulations show that the proposed algorithm is more robust to imprecise distances as compared to existing algorithms.
Learning automaton (LA) is one of the reinforcement learning techniques in artificial intelligence. Learning automata’s learning ability in unknown environments is a useful technique for modeling, controlling, and solving many real problems in the distributed and decentralized environments. In this chapter, first, we provide an overview of LA concepts and recent variants of LA models. Then, we present a brief description of the recent reinforcement learning mechanisms for solving optimization problems. Finally, the evolution of the recent LA models for optimization is presented.
Cellular learning automaton (CLA), as one of the artificial intelligence techniques in reinforcement learning, is a combination of cellular automata (CA) and learning automata (LA). Since CLA has both the computational power of cellular automata and the learning ability of learning automata, it is a useful technique for modeling, controlling, and solving many real problems in the unknown, distributed, and decentralized environments. In this chapter, first, we provide an overview of CA and LA, then we focus on the recent variants of CLA models such as asynchronous CLA, open synchronous CLA, irregular CLA, dynamic irregular CLA, heterogeneous dynamic irregular CLA, wavefront CLA, and associative CLA. Finally, we present brief descriptions of the recent applications of the different models of CLA for solving real problems.
Cellular learning automaton (CLA) is the combination of cellular automaton (CA) and learning automaton (LA), which inherit both the distributed computation capability of the CA and learning ability of the LA in the non-deterministic environments. Due to powerful features of the CLA, they have found many applications in areas of graph theory, computer networks, cellular networks, p2p networks, mech networks, social network analysis, data mining, evolutionary computing, cloud computing, to mention a few. In this chapter, we aim to investigate all research results and address the potential trends of the critical studies related to cellular learning automaton models as one of the recent reinforcement learning approaches. In this regard, first, we evaluate 109 papers indexed in Web of Science (WoS) and 143 papers indexed in Scopus until April 2020. We identify the top contributing authors, institutes, countries, journals, and key research topics based on the techniques of bibliometric network analysis.
