b masoumi | Qiau - Academia.edu (original) (raw)

Papers by b masoumi

In this paper an improved approach based on CBR-LA model is proposed for static task assignment i... more In this paper an improved approach based on CBR-LA model is proposed for static task assignment in heterogeneous computing systems. The proposed model is composed of case based reasoning (CBR) and learning automata (LA) techniques. The LA is used as an adaptation mechanism that adapts previously experienced cases to the problem which must be solved (new case). The goal of this paper is to expressing some weak points of the CBR-LA and proposing new algorithm called ICBR-LA which has improved performance in terms of Makespan performance metric. The results of experiments have shown that the proposed model performs better than the previous one.

Journal of AI and Data Mining, 2020

Biogeography-Based Optimization (BBO) algorithm has recently been of great interest to researcher... more Biogeography-Based Optimization (BBO) algorithm has recently been of great interest to researchers for simplicity of implementation, efficiency, and the low number of parameters. The BBO Algorithm in optimization problems is one of the new algorithms which have been developed based on the biogeography concept. This algorithm uses the idea of animal migration to find suitable habitats for solving optimization problems. The BBO algorithm has three principal operators called migration, mutation and elite selection. The migration operator plays a very important role in sharing information among the candidate habitats. The original BBO algorithm, due to its poor exploration and exploitation, sometimes does not perform desirable results. On the other hand, the Edge Assembly Crossover (EAX) has been one of the high power crossovers for acquiring offspring and it increased the diversity of the population. The combination of biogeography-based optimization algorithm and EAX can provide high ...

2016 Artificial Intelligence and Robotics (IRANOPEN), 2016

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), 2014

2010 Second WRI Global Congress on Intelligent Systems, 2010

Rescue Simulation System is an example of multiagent systems in which we encounter many challenge... more Rescue Simulation System is an example of multiagent systems in which we encounter many challenges. One of these challenges is to having Tradeoff between exploration and exploitation in path planning phase. In this paper we present an exploration method based on variable structure S model learning automaton which uses the entropy of action's probability vector as a criteria to give reward or to penalize its selected action. This method can leads agents to establish a logical balance between exploration and exploitation too. The results show that the proposed method has good performance from both exploration and acquired final score point of view in rescue simulation system.

2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA), 2009

Robocup Rescue Simulation System is a suitable test-bed for test and evaluation of multi-agent sy... more Robocup Rescue Simulation System is a suitable test-bed for test and evaluation of multi-agent system's related ideas and techniques. Hence, the world robocup competitions are held each year and the used ideas and techniques are evaluated in the form of different teams. In rescue simulation system, at the start of simulation, police agents should search and explore the earthquake area and open the blocked roads. In this paper, which is the first application of distributed learning automata in rescue simulation system, the agents imitate the ways which real people use for solving traffic problems in their societies. Like the real peoples' solutions for traffic problem, the agents use virtual police and street signs in their virtual environment junctions. The results indicate the fact that the proposed method gives more exploration power to rescue agents.

International Conference on Education and e-Learning Innovations, 2012

ABSTRACT Intelligent Tutorial Systems are educational software packages that occupy Artificial In... more ABSTRACT Intelligent Tutorial Systems are educational software packages that occupy Artificial Intelligence (AI) techniques and methods to represent the knowledge, as well as to conduct the learning interaction. Tutorial-like systems simulates a Socratic model of learning for teaching uncertain course material by simulating the learning process for both Teacher and a School of Students. The Student is the center of attention in any Tutorial system. The proposed method in this paper improves the student's behavior model in a tutorial-Like system. In the proposed method, student model is determined by high level learning automata called Level Determinant Agent (LDA-LAQ), which attempts to characterize and improve the learning model of the students. LDA-LAQ actually use learning automata as a learning mechanism to show how the student is slow, normal or fast in the term of learning. This paper shows the new student how learning model increases speed accuracy using Pursuit learning automata and Reinforcement Learning.

In electronic commerce markets, agents often should acquire multiple resources to fulfil a high-l... more In electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. In recent years, negotiation has been employed to allocate resources in multi-agent systems. Yet, in most of the conventional methods, negotiation is done without considering past experiments. In this paper, in order to use experiments of agents, a hybrid method is used which employed casebased reasoning and learning automata in negotiation. In the proposed method, the buyer agent would determine its seller and its offered price based on the passed experiments and then an offer would be made. Afterwards, the seller would choose one of the allowed actions using learning automata. Results of the experiments indicated that the proposed algorithm has caused an improvement in some performance measures such as success rate.

2013 13th Iranian Conference on Fuzzy Systems (IFSC), 2013

ABSTRACT The issue of Resource Allocation in Heterogeneous systems has been solved by MaxMin, Min... more ABSTRACT The issue of Resource Allocation in Heterogeneous systems has been solved by MaxMin, MinMin and genetic algorithms so they had high Cost. In this paper we try to provide a Combination of Minority Games, LRP Automata and Case Base reasoning, and by using these methods, we reduced costs or Makespan in Resources Allocation Problems. This paper attempts to make changes in the learning of automata for training agents. In the proposed method, we have the MG-ICBR and MG-ICBR-LRP which the first does not use learning automata and the second uses LRP automata. To perform detailed experiments, one type of Case Base is used. Results of experiments show that Cost of allocation in proposed method MG-ICBR-LRP has been decreased and LRP automata acts better.

International Journal of Computer Applications, 2012

In this paper, a new algorithm based on case base reasoning and reinforcement learning is propose... more In this paper, a new algorithm based on case base reasoning and reinforcement learning is proposed to increase the rate convergence of the reinforcement learning algorithms in multiagent systems. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function has been proposed to select the action, which has led to an increase in algorithms based on Q-learning. The algorithm mentioned has been used for solving the problem of cooperative Markov's games as one of the models of Markov based multiagent systems. The results of experiments have shown that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

2011 IEEE Symposium on Computers & Informatics, 2011

Expert Systems with Applications, 2011

Learning automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinfor... more Learning automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms and are able to control the stochastic games. In this paper, the concepts of stigmergy and entropy are imported into learning automata based multi-agent systems with the purpose of providing a simple framework for interaction and coordination in multi-agent systems and speeding up the learning process. The multi-agent system considered in this paper is designed to find optimal policies in Markov games. We consider several dummy agents that walk around in the states of the environment, make local learning automaton active, and bring information so that the involved learning automaton can update their local state. The entropy of the probability vector for the learning automata of the next state is used to determine reward or penalty for the actions of learning automata. The experimental results have shown that in terms of the speed of reaching the optimal policy, the proposed algorithm has better learning performance than other learning algorithms.

Asian Journal of Control, 2012

Markov games, as the generalization of Markov decision processes to the multi agent case, have lo... more Markov games, as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi-agent systems. The Markov game view of MAS is considered as a sequence of games having to be played by multiple players while each game belongs to a different state of the environment. In this paper, several learning automata based multi-agent system algorithms for finding optimal policies in Markov games are proposed. In all of the proposed algorithms, each agent residing in every state of the environment is equipped with a learning automaton. Every joint-action of the set of learning automata in each state corresponds to moving to one of the adjacent states. Each agent moves from one state to another and tries to reach the goal state. The actions taken by learning automata along the path traversed by the agent are then rewarded or penalized based on the comparison of the average reward received by agent per move along the path with a dynamic threshold. In the second group of the proposed algorithms, the concept of entropy has been imported into learning automata based multi-agent systems to improve the performance of the algorithms. To evaluate the performance of the proposed algorithms, computer experiments have been conducted. The results of experiments have shown that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

In this paper, a new algorithm based on case base reasoning and reinforcement learning is propose... more In this paper, a new algorithm based on case base reasoning and reinforcement learning is proposed to increase the rate convergence of the Selfish Q-Learning algorithms in multi-agent systems. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function has been proposed to select the action, which has led to an increase in algorithms based on Selfish Q-learning. The algorithm mentioned has been used for solving the problem of cooperative Markov's games as one of the models of Markov based multi-agent systems. The results of experiments on two ground have shown that the proposed algorithm perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

Markov games, as the generalization of Markov decision processes to the multi agent case, have lo... more 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 the states of the problem, and the directed edges represent the actions that result in transition from one state to another. Each state of the environment is equipped with a variable structure learning automata whose actions are moving to different adjacent states of that state. Each agent moves from one state to another and tries to reach the goal state. In each state, the agent chooses its next transition with help of the learning automaton in that state. The actions taken by learning automata along the path traveled by the agent is then rewarded or penalized based on the value of the traveled path according to a learning algorithm. In the second group of the proposed algorithms, the concept of entropy has been imported into learning automata based multi-agent systems to drive the magnitude of the reinforcement signal given to the LA and improve the performance of the algorithms. The results of experiments have shown that the proposed algorithms perform better than the existing learning automata based algorithms in terms of speed and the accuracy of reaching the optimal policy. Streszczenie. Zaprezentowano szereg automatów uczących bazujących na algorytmach systemów typu multi-agent w celu poszukiwania optymalnej polityki w kooperatywnej grze Markova. Proces Markova jest opisany w postaci grafów których węzły opisują stan problemu, a krawędzie reprezentują akcje. (Automat uczący bazujący na algorytmie znajdowania optymalnej strategii w kooperacyjne grze Markova)

Journal of AI and Data Mining, 2020

2016 Artificial Intelligence and Robotics (IRANOPEN), 2016

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), 2014

2010 Second WRI Global Congress on Intelligent Systems, 2010

2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA), 2009

International Conference on Education and e-Learning Innovations, 2012

2013 13th Iranian Conference on Fuzzy Systems (IFSC), 2013

International Journal of Computer Applications, 2012

2011 IEEE Symposium on Computers & Informatics, 2011

Expert Systems with Applications, 2011

Asian Journal of Control, 2012

In this paper, a new algorithm based on case base reasoning and reinforcement learning is propose... more In this paper, a new algorithm based on case base reasoning and reinforcement learning is proposed to increase the rate convergence of the Selfish Q-Learning algorithms in multi-agent systems. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function has been proposed to select the action, which has led to an increase in algorithms based on Selfish Q-learning. The algorithm mentioned has been used for solving the problem of cooperative Markov's games as one of the models of Markov based multi-agent systems. The results of experiments on two ground have shown that the proposed algorithm perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

Markov games, as the generalization of Markov decision processes to the multi agent case, have lo... more 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 the states of the problem, and the directed edges represent the actions that result in transition from one state to another. Each state of the environment is equipped with a variable structure learning automata whose actions are moving to different adjacent states of that state. Each agent moves from one state to another and tries to reach the goal state. In each state, the agent chooses its next transition with help of the learning automaton in that state. The actions taken by learning automata along the path traveled by the agent is then rewarded or penalized based on the value of the traveled path according to a learning algorithm. In the second group of the proposed algorithms, the concept of entropy has been imported into learning automata based multi-agent systems to drive the magnitude of the reinforcement signal given to the LA and improve the performance of the algorithms. The results of experiments have shown that the proposed algorithms perform better than the existing learning automata based algorithms in terms of speed and the accuracy of reaching the optimal policy. Streszczenie. Zaprezentowano szereg automatów uczących bazujących na algorytmach systemów typu multi-agent w celu poszukiwania optymalnej polityki w kooperatywnej grze Markova. Proces Markova jest opisany w postaci grafów których węzły opisują stan problemu, a krawędzie reprezentują akcje. (Automat uczący bazujący na algorytmie znajdowania optymalnej strategii w kooperacyjne grze Markova)