Behrooz Masoumi

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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(More)
Markov games, as the generalization of Markov decision processes to the multi-agent case, have long been used for modeling multi-agent systems (MAS). 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(More)
RoboCup Rescue Simulation System is a suitable testbed for test and evaluation of multiagent system’s related ideas and techniques. Hence, the world RoboCup competitions is hold each year and the used ideas and techniques are evaluated in the form of different teams. Almost all of participated teams in the world RoboCup competitions use the shortest path(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 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(More)
Text summarization is the objective extraction of some parts of the text, such as sentence and paragraph, as the document abstract. If there are documents with a large amount of information, extractive text summarization would be arisen as an NP-complete problem. To solve these problems, metaheuristic algorithms are used. In this paper, a method based on(More)
At this article, we study the solutions about resources allocation in grid systems by means of synthetic method of collective case based on reasoning and learning automata. The collective case based on reasoning method in relation to agents, solves problems according to main agent method as scheduler agent. In fact, scheduler agent chooses a method within(More)
There is ongoing research on the applications of intelligent agents in economic systems. This includes optimal pricing of the goods for the sellers and finding the best options for the buyers. To analyze such systems, the market is often modeled as a game providing the benefits of well-known game theoretic models. However, one assumption in most of the(More)
Rescue Simulation System is an example of multi-agent 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(More)
Autonomously navigation of mobile robots in unknown environments is a basic challenge in robotics. We can use behavior based approach in navigation of mobile robots in environments with obstacles. If actions of robot be taken as behavior, we can design them by fuzzy logic. It decreases the problem states, make the navigation easier and also can be used as(More)