In this paper, we introduce open cellular LEARNING AUTOMATA and then study its convergence behavior. It is shown that for a class of rules called commutative rules, the open cellular LEARNING AUTOMATA in stationary external environments converges to a stable and compatible configuration. The numerical results also confirm the theory.  

Background and Objectives: Sonar data processing is used to identify and track targets whose echoes are unsteady. So that they aren’t trusty identified in typical tracking methods. Recently, RLA have effectively cured the accuracy of undersea objective detection compared to conventional sonar objective cognition procedures, which have robustness and low accuracy. Methods: In this research, a combination of classifiers has been used to improve the accuracy of sonar data classification in complex problems such as identifying marine targets. These classifiers each form their pattern on the data and store a model. Finally, a weighted vote is performed by the LA algorithm among these classifiers, and the classifier that gets the most votes is the classifier that has had the greatest impact on improving performance parameters.Results: The results of SVM, RF, DT, XGboost, ensemble method, R-EFMD, T-EFMD, R-LFMD, T-LFMD, ANN, CNN, TIFR-DCNN+SA, and joint models have been compared with the proposed model. Considering that the objectives and databases are different, we benchmarked the average detection rate. In this comparison, Precision, Recall, F1_Score, and Accuracy parameters have been considered and investigated in order to show the superior performance of the proposed method with other methods.Conclusion: The results obtained with the analytical parameters of Precision, Recall, F1_Score, and Accuracy compared to the latest similar research have been examined and compared, and the values are 87.71%, 88.53%, 87.8%, and 87.4% respectively for each of These parameters are obtained in the proposed method.

In this paper, a new algorithm which is the result of combination of cellular LEARNING AUTOMATA (CLA) and shuffled frog leap algorithm (SFLA) is proposed for optimization of functions in continuous, static environments. In the frog leaping algorithm, every frog represents a feasible solution within the problem space. In the proposed algorithm, each memeplex of frogs is placed in a cell of CLA. LEARNING AUTOMATA in each cell acts as the brain of memeplex and will determine the strategy of motion and search. The proposed algorithm along with the standard SFLA and two global and local versions of particle swarm optimization algorithm have been tested in 30-dimensional space on five standard merit functions. Experimental results show that the proposed algorithm has a performance of the introduced algorithm is due to the control of search behavior of frogs during the optimization process.

Social networks are one of the types of complex networks. Identifying communities in social networks is an effective way to use their information, for which several algorithms have been presented so far. In this paper, novel algorithms are designed, in which a LEARNING automaton is attached to each node; The number of actions of LEARNING AUTOMATA is fixed and equal to the estimate of the number of network communities. At each step, each of the LEARNING AUTOMATA chooses an action from its set of actions. Choosing any of these actions means assigning the label of that community to the node. The action chosen by each automaton is evaluated based on the chosen actions of its neighbors ((local attention) and/or communities detected by the entire method (global screening). The result of the evaluation leads to generate rewards or punish signal for the AUTOMATA. By receiving a reward, the probability of re-choosing the chosen action by the automaton, or the community label, increases, and otherwise, by receiving a fine, the probability of this action decreases. By repeating the algorithm, the optimal action is determined as long as no change occurs in the selected label of the corresponding AUTOMATA of each node with more iterations, and as a result, the optimal communities are determined as the output of the algorithm. The comparison of the results of the experiments shows the effectiveness of the proposed methods in comparison with the previous methods.

Agents are software entities that act continuously and autonomously in a special environment. It is very essential for the agents to have the ability to learn how to act in the special environment for which they are designed to act in, to show reflexes to their environment actions, to choose their way and pursue it autonomously, and to be able to adapt and learn. In multi-agent systems, many intelligent agents that can interact with each other cooperate to achieve a set of goals. Because of the inherent complexity that exists in dynamic and changeable multi-agent environments, there is always a need to machine LEARNING in such environments. As a model for LEARNING, LEARNING AUTOMATA act in a stochastic environment and are able to update their action probabilities considering the inputs from their environment, so optimizing their functionality as a result. LEARNING AUTOMATA are abstract models that can perform some numbers of actions. Each selected action is evaluated by a stochastic environment and a response is given back to the AUTOMATA. LEARNING AUTOMATA use this response to choose its next action. In this paper, the goal is to investigate and evaluate the application of LEARNING AUTOMATA to cooperation in multi-agent systems, using soccer server simulation as a test-bed. Because of the large state space of a complex Multi-agent domains, it is vital to have a method for environmental states’ generalization. An appropriate selection of such a method can have a great role in determining agent states and actions. In this paper we have also introduced and designed a new technique called “The best corner in State Square” for generalizing the vast number of states in the environment to a few number of states by building a virtual grid in agent’s domain environment. The efficiency of this technique in a cooperative multi-agent domain is investigated.

Active database systems (ADBS) can react to the occurrence of predefined events automatically by definition a collection of active rules. One of the most important modules of ADBS is the rule scheduler, which has considerable impact on performance and efficiency of ADBS. The job of rule scheduler is the selection of a rule for execution from the set of ready for execution rules. In this paper, we propose a new approach based on LEARNING AUTOMATA to improve the rule scheduling performance in terms of average response time, response time variance, and throughput. LEARNING AUTOMATA have been used to obtain better estimations for rule execution probabilities. The results of experimentations show that the performance of the proposed method outperforms the most effective existing rule scheduling method.

Variety of methods for traffic policing such as windowing algorithm and leaky bucket for computer networks have been proposed in the literatures. The windowing algorithm works well for packet networks but it is not appropriate for High speed networks such as ATM. In this paper, a new group of traffic policers for ATM network has been proposed. These proposed traffic policers are obtained by improving the leaky bucket method. LEARNING AUTOMATA is used to adapt the parameters of the leaky bucket according to the behavior of the traffic sources. To study the effectiveness of the proposed methods, computer simulations have been conducted. The results of simulation have shown that the proposed methods reduce the percentage of the cell loss. The proposed traffic policer works well for the situations where the traffic characteristics. Of the source are unknown.