In previous papers authors have considered agents as inertia less self driven particles and designed a flocking algorithm. Application of this algorithm to agents with considerable inertial characteristics needs a behavioural controller. The controller uses the local information and helps every agent to imitate the desired behaviour as a member of the flocking frame which covers the main issue in this paper. All agents are assumed to possess limited identical influencing/sensing radius. The sliding-mode control technique is used, hence; effect of bounded disturbances and uncertainties can be omitted too. Once inertial agents are equipped with the behavioral controller, the Multi-agent system behaves similar to a group of self-driven inertia-less particles which; coordination control algorithms and cohesion analyses are previously designed for.

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.

agents, in a Multi agent system, communicate with each other through the process of exchanging messages which is called dialogue. Multi agent organization is generally usedto optimize agents’ communications. Holonic organization demonstrates a self-similar recursive and hierarchical structure in which each holon may include some other holons. In a holonic system, lateral communication occurs between members of a determined holon and vertical communications are inter-level ones between different holons. When agents start a dialogue, according to their beliefs, they follow some rules that define the permissive speech acts called dialogue protocol. The dialogue strategy is the policy of agents to choose a particular speech act among the allowed ones by the protocol in order to achieve the common goals of holon. In this paper a formal model for dialogue strategy for lateral communication in a holon is proposed. This model tries to choose the most preferable speech acts considering at the same time local beliefs and goals along with public knowledge obtained from holonic organization. Moreover, the argumentation theory is applied to rank and define the values of speech acts. The proposed model finds the most preferable option to utter and it also decreases the number of exchanging messages. The proposed model of dialogue strategy is illustrated via a deliberation dialogue example in a holon. The example showed a significant efficiency in decreasing the number of exchanged messages and the effectiveness of deliberation.

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a Multi-agent system that combines Multiple intelligences such as: Multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Due to their extended use, nowadays, Multi-agent systems have attracted the attention of many researchers. Bipartite formation is one of the interesting fields of Multi-agent systems that has been raised within a decade. In this research, the distributed bipartite formation of Multi-agent systems was investigated by a leader-follower approach. Heterogeneous agents in this approach have second-order nonlinear dynamics, and the communication graph between the agents is also a structurally balanced signed directed graph. Because of the distributed approach of the controller and the lack of existing communication between the leader and all of the followers, a finite-time observer is used to estimate the leader's dynamic in finite time. Also, because a finite-time approach increases performance and efficiency in many practical applications, a terminal sliding mode controller provides finite-time formation. Finally, the validity of the theoretical results is illustrated by simulation examples.

This paper describes how Multi-agent system technology can be used as the underpinning platform for voltage control in power systems. In this study, some FACTS (flexible AC transmission systems) devices are properly designed to coordinate their decisions and actions in order to provide a coordinated secondary voltage control mechanism based on Multi-agent theory. Each device here is modeled as an agent being able to cooperate and communicate with other devices. In this system, individual autonomous agents and intelligent decision makers learn to perform optimal actions through proper interactions with their environments. The SARSA Q-learning, which is an on-policy algorithm in reinforcement learning (RL) is then used and tested successfully in voltage control problem. In this research, the Java agent Development (JADE) platform is used to implement the agents and to simulate their communications. The power system is also fully implemented in Java. The proposed intelligent MA based method is finally applied to IEEE 39-buses New England power system. The results of simulation better highlight the merit of the method and its ability in coordinating FACTS devices for removing voltage disturbances.

This paper investigates a novel method to solve distributed optimization problems in the presence of communication delays between the networked agents that cooperate together to find an optimal solution of a global cost function composed of local ones. In the problem of distributed optimization in a network of Multi-agent because of existing phenomena such as communication delay, deriving approaches having appropriate performance so that the states of all agents converge to the same value always has been a substantial challenge. Delay-dependent conditions in the form of linear matrix inequities are derived to analyze the convergence of the introduced scheme to the optimal solution. It is demonstrated that the maximum allowable time delay in the network and convergence rate of the optimization procedure are increased by the suggested strategy. Finally, comparative simulation results are considered to illustrate the superior performance of the introduced scheme compared to a rival one in the literature.

This paper presents a cooperative control which is applied to the secondary control of a microgrid controlled via a Multi-agent scheme. Balancing power that leads to voltage and frequency stability in a microgrid is essential. The voltage and frequency regulations are limiting within the specified limits and conveying them to their nominal values. Limiting and conveying the voltage and frequency to their nominal values is done by the primary and secondary controls, respectively. A Microgrid has both dispatchable and non-dispatchable sources. Dispatchable sources are controlled by the conventional P-ω and Q-E droop controls. A photovoltaic as a non-dispatchable source generates the active power according to weather conditions, but the reactive power is supplied using the E-Q droop method. The E-Q droop uses the idle capacity of the inverters in the reactive power supply. Distributed secondary control increases the stability due to good, accurate and reliable controls. The frequency is constant in the whole microgrid. Since line impedances are different, load terminal voltage control is necessary. The load is considered as another agent, who can request the desired voltage at its terminal bus.