Abstract: The body of available information is rapidly increasing every day. It is becoming impossible for humans to peruse all the available information and extract the data related to a specific topic. A great deal of important information is being neglected primarily for two major reasons. Firstly, highly relevant information is losing its true meaning within a pool of insignificant and unauthorized information. A lot of available information is published which is of no significance and sometimes may even be destructive and corruptive in nature. Secondly, new knowledge and discoveries are emerging quickly as we continue to progress and new technologies emerge. It is becoming more and more difficult each day to find specific information even within a body of authorized information.

Abstract: All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE Transactions on Automatic Control paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given.

Abstract: In this work, we consider the controlled agreement problem for multi-agent networks, where a collection of agents take on leader roles while the remaining agents execute local, consensus-like protocols. Our aim is to identify reflections of graph-theoretic notions on system-theoretic properties of such systems. In particular, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the controllability of the corresponding multi-agent system. Moreover, we introduce network equitable partitions as a means by which such controllability characterizations can be extended to the multileader setting.

Abstract: The objective of this paper is to discuss the design and implementation of a multi-agent system that provides intelligence to a distributed smart grid — a smart grid located at a distribution level. A multi-agent application development will be discussed that involves agent specification, application analysis, application design and application realization. The message exchange in the proposed multi-agent system is designed to be compatible with an IP-based network (IP = Internet Protocol) which is based on the IEEE standard on Foundation for Intelligent Physical Agent (FIPA). The paper demonstrates the use of multi-agent systems to control a distributed smart grid in a simulated environment. The simulation results indicate that the proposed multi-agent system can facilitate the seamless transition from grid connected to an island mode when upstream outages are detected. This denotes the capability of a multi-agent system as a technology for managing the microgrid operation.

Abstract: A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.

Abstract: We present a view of cooperative control using the language of learning in games. We review the game-theoretic concepts of potential and weakly acyclic games, and demonstrate how several cooperative control problems, such as consensus and dynamic sensor coverage, can be formulated in these settings. Motivated by this connection, we build upon game-theoretic concepts to better accommodate a broader class of cooperative control problems. In particular, we extend existing learning algorithms to accommodate restricted action sets caused by the limitations of agent capabilities and group based decision making. Furthermore, we also introduce a new class of games called sometimes weakly acyclic games for time-varying objective functions and action sets, and provide distributed algorithms for convergence to an equilibrium.

Abstract: We study a model of opinion dynamics introduced by Krause: each agent has an opinion represented by a real number, and updates its opinion by averaging all agent opinions that differ from its own by less than one. We give a new proof of convergence into clusters of agents, with all agents in the same cluster holding the same opinion. We then introduce a particular notion of equilibrium stability and provide lower bounds on the inter-cluster distances at a stable equilibrium. To better understand the behavior of the system when the number of agents is large, we also introduce and study a variant involving a continuum of agents, obtaining partial convergence results and lower bounds on inter-cluster distances, under some mild assumptions.

Abstract: In this note, we study consensus problems for continuous-time multi-agent systems in directed networks with dynamically changing topologies and nonuniform time-varying delays. We have analyzed consensus problems in the following three cases: 1) directed networks with dynamically changing topologies and nonuniform time-varying delays; 2) directed networks with intermittent communication and data packet dropout; and 3) finite-time consensus in directed networks with dynamically changing topologies and nonuniform time-varying delays. We propose a new approach based on a tree-type transformation to investigate consensus problems in all three cases. Some necessary and/ or sufficient conditions are established. Simulation results are also given to demonstrate the theoretical results.

Abstract: Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, mitigating the threat of bio-warfare, and understanding the factors that may be responsible for the fall of ancient civilizations. Such progress suggests the potential of ABMS to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories. Some contend that ABMS "is a third way of doing science" and could augment traditional deductive and inductive reasoning as discovery methods. This brief tutorial introduces agent-based modeling by describing the foundations of ABMS, discussing some illustrative applications, and addressing toolkits and methods for developing agent-based models.

Abstract: Event-driven strategies for multi-agent systems are motivated by the future use of embedded microprocessors with limited resources that will gather information and actuate the individual agent controller updates. The control actuation updates considered in this paper are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state, and are applied to a first order agreement problem. A centralized formulation of the problem is considered first and then the results are extended to the decentralized counterpart, in which agents require knowledge only of the states of their neighbors for the controller implementation.

Abstract: Agent Based Modeling (ABM) toolkits are as diverse as the community of people who use them. With so many toolkits available, the choice of which one is best suited for a project is left to word of mouth, past experiences in using particular toolkits and toolkit publicity. This is especially troublesome for projects that require specialization. Rather than using toolkits that are the most publicized but are designed for general projects, using this paper, one will be able to choose a toolkit that already exists and that may be built especially for one's particular domain and specialized needs. In this paper, we examine the entire continuum of agent based toolkits. We characterize each based on 5 important characteristics users consider when choosing a toolkit, and then we categorize the characteristics into user-friendly taxonomies that aid in rapid indexing and easy reference.

Abstract: The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent However, the "agentification" of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them

Abstract: In this paper, we address the problem of driving a group of agents towards a consensus point when the agents have a discrete-time single- or double-integrator dynamics and the communication network is time-varying. We propose decentralized model predictive control schemes that take into account constraints on the agents' input and show that they guarantee consensus under mild assumptions. Since the global cost does not decrease monotonically, it cannot be used as a Lyapunov function for proving convergence to consensus. For this reason, our proofs exploit geometric properties of the optimal path followed by individual agents.

Abstract: The term computer simulation is related to the usage of a computational model in order to improve the understanding of a system's behavior and/or to evaluate strategies for its operation, in explanatory or predictive schemes. There are cases in which practical or ethical reasons make it impossible to realize direct observations: in these cases, the possibility of realizing 'in-machina' experiments may represent the only way to study, analyze and evaluate models of those realities. Different situations and systems are characterized by the presence of autonomous entities whose local behaviors (actions and interactions) determine the evolution of the overall system; agent-based models are particularly suited to support the definition of models of such systems, but also to support the design and implementation of simulators. Agent-Based models and Multi-Agent Systems (MAS) have been adopted to simulate very different kinds of complex systems, from the simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, from biological systems to urban planning. This paper discusses the specific aspects of this approach to modeling and simulation from the perspective of Informatics, describing the typical elements of an agent-based simulation model and the relevant research.

Abstract: Methodological Guidelines for Modeling and Developing MAS-Based Simulations The intersection of agents, modeling, simulation, and application domains has been the subject of active research for over two decades Although agents and simulation have been used effectively in a variety of application domains, much of the supporting research remains scattered in the literature, too often leaving scientists to develop multi-agent system (MAS) models and simulations from scratch Multi-Agent Systems: Simulation and Applications provides an overdue review of the wide ranging facets of MAS simulation, including methodological and application-oriented guidelines This comprehensive resource reviews two decades of research in the intersection of MAS, simulation, and different application domains It provides scientists and developers with disciplined engineering approaches to modeling and developing MAS-based simulations After providing an overview of the fields history and its basic principles, as well as cataloging the various simulation engines for MAS, the book devotes three sections to current and emerging approaches and applications Simulation for MAS explains simulation support for agent decision making, the use of simulation for the design of self-organizing systems, the role of software architecture in simulating MAS, and the use of simulation for studying learning and stigmergic interaction MAS for Simulation discusses an agent-based framework for symbiotic simulation, the use of country databases and expert systems for agent-based modeling of social systems, crowd-behavior modeling, agent-based modeling and simulation of adult stem cells, and agents for traffic simulation Tools presents a number of representative platforms and tools for MAS and simulation, including Jason, James II, SeSAm, and RoboCup Rescue Complete with over 200 figures and formulas, this reference book provides the necessary overview of experiences with MAS simulation and the tools needed to exploit simulation in MAS for future research in a vast array of applications including home security, computational systems biology, and traffic management

Abstract: Our increasing societal demand for mobility now challenges researchers to devise more efficient traffic and transportation systems.The Handbook of Research on Multi-Agent Systems for Traffic and Transportation Engineering provides a unique compendium of research covering topics such as transportation system designs, control devices, and techniques to optimize existing networks. Presenting a collection of approaches to issues in traffic and transportation, this authoritative reference offers a compilation of chapters with innovative methods and systems written by leading international researchers.

Abstract: The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. The traditional design model for GGP agents has been to use a minimax-based game-tree search augmented with an automatically learned heuristic evaluation function. The first successful GGP agents all followed that approach. In this paper, we describe CadiaPlayer, a GGP agent employing a radically different approach: instead of a traditional game-tree search, it uses Monte Carlo simulations for its move decisions. Furthermore, we empirically evaluate different simulation-based approaches on a wide variety of games, introduce a domain-independent enhancement for automatically learning search-control knowledge to guide the simulation playouts, and show how to adapt the simulation searches to be more effective in single-agent games. CadiaPlayer has already proven its effectiveness by winning the 2007 and 2008 Association for the Advancement of Artificial Intelligence (AAAI) GGP competitions.

Abstract: Autonomous agents and multiagent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can't be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.

Abstract: In this article, the formation control is investigated for a network of second-order dynamic agents with heterogeneous communication delays. The desired stationary formation is achieved by introducing diverse self-delay for each agent. In addition, a delay-dependent formation control algorithm is proposed to achieve the desired moving formation. Based on the frequency-domain analysis and matrix theory, sufficient conditions are obtained for the multi-agent systems asymptotically converging to desired stationary and moving formations, respectively. Simulation results illustrate the correctness of the results.

Abstract: In most multiagent systems planning on forehand can help to seriously improve the efficiency of executing actions. The main difference between centrally creating a plan and constructing a plan for a system of agents lies in the fact that in the latter coordination plays the main part. This introduces a number of additional difficulties. This special issue discusses some of these difficulties in detail. To place these in a context, this introduction gives a brief overview of multiagent planning problems, and most multiagent planning techniques.

Abstract: We investigate a new consensus problem in networks of dynamic agents, where the agents in a network can reach more than one consistent values asymptotically. It contains such consensus problem as a special case that all agents in a network reach a consistent value asymptotically. When information exchange is undirected, a novel consensus protocol is designed to solve the group consensus problem. The convergence analysis is discussed and several criterions are established based on graph theories and matrix theories. Simulation results are presented to demonstrate the effectiveness of the theoretical results.