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• Introduction.- Preliminaries on System Theory and Optimization Algorithms.- Interaction Topology of Multiagent Systems and Consensus Protocols.- Emergent Behavior Detection in Multiagent Systems.- Distributed Task Coordination of Multiagent Systems.- Multiagent Task Coordination Using Approximate Dynamic Programming.- Distributed Gradient Estimation for Multiagent Task Optimization.- Case Studies and Experiments.
• (source: Nielsen Book Data)

This book addresses problems in the modeling, detection, and control of emergent behaviors and task coordination in multiagent systems. It presents a unified solution to such problems in terms of distributed estimation, distributed control, and optimization of interaction topologies and dynamics. Four aspects of the technical solutions in the book are presented: First, the impact of interaction dynamics on the convergence conditions related to interaction topologies is discussed, utilizing a discontinuous cooperative control algorithm of updated design. Second, distributed least-squares and Kalman filtering algorithms for agents with limited interactions are elaborated upon. Third, a general framework of distributed nonlinear control is established, and distributed adaptive control for nonlinear systems with more general uncertainties is presented. Based on the proposed framework, a distributed nonlinear controller is designed to deal with task coordination of robotic systems with nonholonomic constraints. Finally, the problem of optimal multiagent task coordination is addressed and solutions based on approximate dynamic programming and approximate distributed gradient estimation are presented. Emergent Behavior Detection and Task Coordination for Multiagent Systems is of interest to practicing engineers in areas such as robotics and cyber-physical systems, researchers in the field of systems, controls, and robotics, and senior undergraduate and graduate students.
(source: Nielsen Book Data)

• 1.
• Introduction
• 2.
• Distributed Control Scheme for Online Workload Partition
• 3.
• Decentralized Cooperative Sweep Coverage Algorithm in Uncertain Environments
• 4.
• Adaptive Cooperative Coverage Algorithm with Online Learning Strategies
• 5.
• Distributed Sweep Coverage Algorithm using Workload Memory
• 6.
• Cooperative Sweep Coverage Algorithm of Discrete Time Multi-Agent Systems
• 7.
• Coverage-Based Cooperative Interception against Supersonic Flight Vehicles
• 8.
• Coverage-Based Cooperative Routing Algorithm for Unmanned Ground Vehicles
• 9.
• Cooperative Coverage Control of Wireless Sensor Networks for Environment Monitoring
• 10. Summary and Future Work.
• (source: Nielsen Book Data)

This book highlights cooperative coverage control approaches of multi-agent systems in uncertain environments and their applications in various fields. A novel theoretical formulation of multi-agent coverage is proposed to fulfill the coverage task via divide-and-conquer scheme. By taking workload partition and sweeping operations simultaneously, a distributed sweep coverage algorithm of multi-agent systems is developed to cooperatively complete the workload on the given region, and its input-to-state stability is guaranteed in theory. Moreover, the coverage performance is evaluated by estimating the error between the actual coverage time and the optimal time. Three application scenarios are presented to demonstrate the advantages of cooperative coverage control approaches in missile interception, intelligent transportation systems and environment monitoring, respectively.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Multi-agent systems with continuously switching topologies
• 3. Multi-agent systems with continuously switching topologies based on polytopic model
• 4. High-order multi-agent systems with time-delays and continuously switching topologies based on polytopic model
• 5. Sliding mode control for multi-agent systems with continuously switching topologies based on polytopic model
• 6. Cooperative relay tracking strategy for multi-agent systems with assistance of Voronoi diagrams
• 7. Stability of a class of multi-agent relay tracking systems with unstable subsystems
• 8. Multi-agent relay tracking systems with time-varying number of tracking agents
• 9. Multi-agent relay tracking systems with time-varying number of tracking agents and time delays
• 10. Stability analysis of nonlinear multi-agent relay tracking systems over a finite time interval
• 11. Conclusion.
• (source: Nielsen Book Data)

Consensus Tracking of Multi-agent Systems with Switching Topologies takes an advanced look at the development of multi-agent systems with continuously switching topologies and relay tracking systems with switching of agents. Research problems addressed are well defined and numerical examples and simulation results are given to demonstrate the engineering potential. The book is aimed at advanced graduate students in control engineering, signal processing, nonlinear systems, switched systems and applied mathematics. It will also be a core reference for control engineers working on nonlinear control and switched control, as well as mathematicians and biomedical engineering researchers working on complex systems.
(source: Nielsen Book Data)

• Part One - About Cooperative Control
• 1. Introduction
• 2. Preliminaries Part Two - Optimal Cooperative Control
• 3. Optimal consensus control of multiple integrator systems
• 4. Optimal cooperative tracking and flocking of multi-agent systems
• 5. Optimal formation control of multiple UAVs
• 6. Optimal coverage control of multi-robot systems Part Three - Robust Cooperative Control
• 7. Robust consensus control of multi-agent systems with input delay
• 8. Robust consensus control of multi-agent systems with disturbance rejection
• 9. Robust consensus control nonlinear p-order integrator systems
• 10. Robust cooperative control of networked negative-imaginary systems.
• (source: Nielsen Book Data)

Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective reports and encourages technology transfer in the field of cooperative control of multi-agent systems. The book deals with UGVs, UAVs, UUVs and spacecraft, and more. It presents an extended exposition of the authors' recent work on all aspects of multi-agent technology. Modelling and cooperative control of multi-agent systems are topics of great interest, across both academia (research and education) and industry (for real applications and end-users). Graduate students and researchers from a wide spectrum of specialties in electrical, mechanical or aerospace engineering fields will use this book as a key resource.
(source: Nielsen Book Data)

• Chapter 1. Overview of Distributed Average Tracking.-
• Chapter 2. Preliminaries.-
• Chapter 3. Distributed Average Tracking via Nonsmooth Feedback.-
• Chapter 4. Distributed Average Tracking via an Extended PI Scheme.-
• Chapter 5. Distributed Average Tracking for Double-Integrator Dynamics.-
• Chapter 6. Distributed Average Tracking for General Linear Dynamics.-
• Chapter 7. Distributed Average Tracking for Euler-Lagrange Dynamics.-
• Chapter 8. Distributed Average Tracking with Input Saturation.
• (source: Nielsen Book Data)

This book presents a systematic study of an emerging field in the development of multi-agent systems. In a wide spectrum of applications, it is now common to see that multiple agents work cooperatively to accomplish a complex task. The book assists the implementation of such applications by promoting the ability of multi-agent systems to track - using local communication only - the mean value of signals of interest, even when these change rapidly with time and when no individual agent has direct access to the average signal across the whole team; for example, when a better estimation/control performance of multi-robot systems has to be guaranteed, it is desirable for each robot to compute or track the averaged changing measurements of all the robots at any time by communicating with only local neighboring robots. The book covers three factors in successful distributed average tracking: algorithm design via nonsmooth and extended PI control; distributed average tracking for double-integrator, general-linear, Euler-Lagrange, and input-saturated dynamics; and applications in dynamic region-following formation control and distributed convex optimization. The book presents both the theory and applications in a general but self-contained manner, making it easy to follow for newcomers to the topic. The content presented fosters research advances in distributed average tracking and inspires future research directions in the field in academia and industry.
(source: Nielsen Book Data)

• Introduction.- Homogeneous Deformation without Inter-Agent Communication.- Homogeneous Deformation under Local Control.- Higher Order Dynamics for MAS Evolution as Continuum Deformation.- Alignment as Biological Inspiration for Control of Multi-Agent Systems.- Deployment of a Multi-Agent System on a Desired Formation.- Appendix A: Kinematics of a Continuum.- Appendix B: Notions of Graph Theory.
• (source: Nielsen Book Data)

This monograph presents new algorithms for formation control of multi-agent systems (MAS) based on principles of continuum mechanics. Beginning with an overview of traditional methods, the author then introduces an innovative new approach whereby agents of an MAS are considered as particles in a continuum evolving in n whose desired configuration is required to satisfy an admissible deformation function. The necessary theory and its validation on a mobile-agent-based swarm test bed are considered for two primary tasks: homogeneous transformation of the MAS and deployment of a random distribution of agents on a desired configuration. The framework for this model is based on homogeneous transformations for the evolution of an MAS under no inter-agent communication, local inter-agent communication, and intelligent perception by agents. Different communication protocols for MAS evolution, the robustness of tracking of a desired motion by an MAS evolving in n, and the effect of communication delays in an MAS evolving under consensus algorithms or homogeneous maps are also explored. Featuring appendices which introduce the requisite concepts from continuum kinematics and graph theory, this monograph will provide advanced graduate students and researchers with the necessary background to understand and apply the methods presented.
(source: Nielsen Book Data)

• Introduction
• Background and Previous Work
• Fairness in Cooperative Multiagent Systems
• Social Optimality in Cooperative Multiagent Systems
• Individual Rationality in Competitive Multiagent Systems
• Social Optimality in Competitive Multiagent Systems
• Conclusion.

This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.

• Preface. Introduction. Theoretical Background. Distributed Intelligence in Power Systems. Consensus for Heterogeneous Systems with Delays. Secure Control of Distributed Multiagent Systems. Advanced Consensus Algorithms. Cooperative Control of Networked Power Systems. Dynamic Graphical Games. References. Index.
• (source: Nielsen Book Data)

Multiagent systems (MAS) are one of the most exciting and the fastest growing domains in the intelligent resource management and agent-oriented technology, which deals with modeling of autonomous decisions making entities. Recent developments have produced very encouraging results in the novel approach of handling multiplayer interactive systems. In particular, the multiagent system approach is adapted to model, control, manage or test the operations and management of several system applications including multi-vehicles, microgrids, multi-robots, where agents represent individual entities in the network. Each participant is modeled as an autonomous participant with independent strategies and responses to outcomes. They are able to operate autonomously and interact pro-actively with their environment. In recent works, the problem of information consensus is addressed, where a team of vehicles communicate with each other to agree on key pieces of information that enable them to work together in a coordinated fashion. The problem is challenging because communication channels have limited range and there are possibilities of fading and dropout. The book comprises chapters on synchronization and consensus in multiagent systems. It shows that the joint presentation of synchronization and consensus enables readers to learn about similarities and differences of both concepts. It reviews the cooperative control of multi-agent dynamical systems interconnected by a communication network topology. Using the terminology of cooperative control, each system is endowed with its own state variable and dynamics. A fundamental problem in multi-agent dynamical systems on networks is the design of distributed protocols that guarantee consensus or synchronization in the sense that the states of all the systems reach the same value. It is evident from the results that research in multiagent systems offer opportunities for further developments in theoretical, simulation and implementations. This book attempts to fill this gap and aims at presenting a comprehensive volume that documents theoretical aspects and practical applications.
(source: Nielsen Book Data)

• Notation and preliminaries.- Part I Synchronization of homogeneous systems.- Synchronization of continuous-time linear MAS.- Synchronization of discrete-time linear MAS.- Synchronization of linear MAS subject to actuator saturation.- Synchronization of continuous-time MAS with nonlinear time-varying agents.- Synchronization of continuous-time linear MAS with unknown input delay.- Synchronization of discrete-time linear MAS with unknown input delay.- Synchronization of continuous-time linear MAS with unknown communication delay.- Synchronization of discrete-time linear MAS with unknown communication delay.- Synchronization of linear MAS subject to actuator saturation and unknown input delay.- Synchronization of continuous-time linear time-varying MAS.- Synchronization of continuous-time nonlinear time-varying MAS.- H1 and H2 almost synchronization of continuous-time linear MAS.- Part II Synchronization of heterogeneous systems.- Necessary conditions for synchronization of heterogeneous MAS.- Regulated output synchronization of heterogeneous continuous-time linear MAS.- Regulated output synchronization of heterogeneous continuous-time nonlinear MAS.- Regulated output synchronization of heterogeneous continuous-time linear time-varying MAS.- Exact regulated output synchronization for heterogeneous continuous-time MAS in the presence of disturbances and measurement noise with known frequencies.- H1 almost output synchronization for heterogeneous continuous-time MAS.- H2 almost regulated output synchronization for heterogeneous continuous-time MAS.- Almost output synchronization of heterogeneous continuous-time linear MAS with passive agents.- Synchronization of heterogeneous continuous-/and discrete-time linear MAS with introspective agents.- A special coordinate basis (SCB) of linear multivariable systems.- Squaring down of general MIMO systems to invertible uniform rank systems via pre- and/or post-compensators.- Index.- References.
• (source: Nielsen Book Data)

This monograph explores the synchronization of large-scale, multi-agent dynamical systems in the presence of disturbances, delays, and time-varying networks. Drawing upon their extensive work in this area, the authors provide a thorough treatment of agents with higher-order dynamics, different classes of models for agents, and the underlying networks representing the agents' actions. The high technical level of their presentation and their rigorous mathematical approach make this a timely and valuable resource that will fill a gap in the existing literature. Divided into two sections, the first part of the book focuses on state synchronization of homogeneous multi-agent systems. The authors consider state synchronization by determining control strategies for both continuous- and discrete-time systems that achieve state synchronization under both full- and partial-state coupling. The chapters that follow examine multi-agent systems with both linear and nonlinear time-varying agents, input-delays for continuous- and discrete-time systems, and communication delays for continuous-time systems. The second part of the book is dedicated to regulated output synchronization of heterogeneous multi-agent systems with linear and nonlinear agents. Both sections of the book include performance considerations in H2- and H-infinity norms in the presence of external disturbances. Research on synchronization of multi-agent systems has been growing in popularity and is highly interdisciplinary, with applications to automobile systems, aerospace systems, multiple-satellite GPS and high-resolution satellite imagery, aircraft formations, highway traffic platooning, industrial process control with multiple processes, and more. Synchronization of Multi-Agent Systems in the Presence of Disturbances and Delays will therefore be of interest to upper-level graduate students, researchers, and engineers in industry working on interconnected dynamical systems.
(source: Nielsen Book Data)

• Norm conflict identification using a convolutional neural network.- Norm violation in online communities - A study of stack overflow comments.- Mining international political norms from the GDELT database.- Developers' responses to app review feedback - A study of communication norms in app development.- Convention emergence in partially observable topologies.- Improving confidence in the estimation of values and norms.- Should I tear down this wall? Optimizing social metrics by evaluating novel actions.- Impact of different belief facets on agents' decision - a refined cognitive architecture to model the interaction between organisations' institutional characteristics and agents' behaviour.- A norm emergence framework for normative MAS.- Social rules for agent systems.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Coordination, Organizations, Institutions, and Norms for Governance of Multi-Agent Systems, COIN 2017, co-located with AAMAS 2017, and the International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems, COINE 2020, co-located with AAMAS 2020. The COIN 2017 workshop was held in Sao Paulo, Brazil, in May 2017 and the COINE 2020 workshop was held virtually, in May 2020. The 9 full papers and 1 short paper were carefully reviewed and selected from a total of 20 submissions for inclusion in this volume and cover the following topics: empirical applications of COINE technologies; emergence and social metrics; and conceptual frameworks and architectures.
(source: Nielsen Book Data)

• Ascending-Price Mechanism for General Multi-Sided Markets.- Governing Black-Box Agents in Competitive Multi-Attribute MAS.- Planning in Non-Uniform Environments for Multi-Agent Pickup and Delivery Tasks.- Revealed preference argumentation framework and applications in consumer behaviour analyses.- Coordinating Multi-Party Vehicle Routing with Location Congestion via Iterative Best Response.- Explaining Ridesharing: Selection of Explanations for Increasing User Satisfaction.- Large-scale, Dynamic and Distributed Coalition Formation with Spatial and Temporal Constraints.- Convention emergence with congested resources.- Aiming for Half Gets You to the Top: Winning PowerTAC 2020.- Parameterized Analysis of Assignment Under Multiple Preferences.- Frameworks and the Preservation of Solid Semantic Properties.- Verification of Multi-Layered Assignment Problems.- Logic and Model Checking by Imprecise Probabilistic Interpreted Systems.- On the Complexity of Predicting Election Outcomes and Estimating Their Robustness.- Point Based Solution Method for Communicative IPOMDPs.- A Decentralized Token-based Negotiation Approach for Multi-Agent Path Finding.
• (source: Nielsen Book Data)

This book constitutes the revised post-conference proceedings of the 18th European Conference on Multi-Agent Systems, EUMAS 2021. The conference was held online in June, 2021. 16 full papers are presented in this volume, each of which carefully reviewed and selected from a total of 51 submissions. The papers report on both early and mature research and cover a wide range of topics in the field of multi-agent systems.
(source: Nielsen Book Data)

• Implementation of Real Data for Financial Market Simulation using Clustering, Deep Learning, and Artificial Financial Market.- Hybrid Dynamic Programming for Simultaneous Coalition Structure Generation and Assignment.- A Socio-Psychological Approach To Simulate Trust and Reputation in Modal Choices.- Reasoning about Trustworthiness in Cyber-Physical Systems Using Ontology-Based Representation and ASP.- Optimal Deterministic Time-based Policy in Automated Negotiation.-Agent Simulation of Collision Avoidance based on Meta-Strategy Model.- The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach.- Distance-based Heuristic Solvers for Cooperative Path Planning with Heterogeneous Agents.-
• Policy Advisory Module for Exploration Hindrance Problem in Multi-agent Deep Reinforcement Learning.- Analysis of Coordination Structures of Partially Observing Cooperative Agents by Multi-Agent Deep Q-Learning.- Policy Adaptive Multi-Agent Deep Deterministic Policy Gradient.- Multi-Agent Planning with High-Level Human Guidance.- Preference Elicitation in Assumption-Based Argumentation.- Declarative Preferences in Reactive BDI Agents.- Predicting the Priority of Social Situations for Personal Assistant Agents.- Mutex Propagation for SAT-based Multi-Agent Path Finding.- A SMT-based Implementation for Safety Checking of Parameterized Multi-Agent Systems.- A Goal-Based Framework For Supporting Medical Assistance: The Case of Chronic Diseases.- Optimal Control of Pedestrian Flows by Congestion Forecasts Satisfying User Equilibrium Conditions.- Automated Negotiation Mechanism and Strategy for Compensational Vehicular Platooning.- A Cognitive Agent Framework in Information Retrieval: Using User Beliefs to Customize Results.- Deep Reinforcement Learning for Pedestrian Guidance.- NegMAS: A platform for automated negotiations.- Simulation of Unintentional Collusion Caused by Auto Pricing in Supply Chain Markets.- Construct an Artificial Population with Urban and Rural Population Differences Considered: to support long-term care system evaluation by agent-based simulation.- Multi-Agent Path Finding with Destination Choice.-
• Abductive Design of BDI Agent-based Digital Twins of Organizations.- Beliefs, Time and Space: A Language for the Ykai Board Game.- Argumentation-based Explanations of Multimorbidity Treatment Plans.- The Persistence of False Memory: Brain in a Vat despite Perfect Clocks.- Box-Office Prediction Based on Essential Features Extracted from Agent-Based Modeling.- Short Duration Aggregate Statistical Model Checking for Multi-Agent Systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020, held in Nagoya, Japan, in November 2020. The 19 full papers presented and 13 short papers were carefully reviewed and selected from 50 submissions.Due to COVID-19, the conference was held online. The conference covers a wide range of ranging from foundations of agent theory and engineering aspects of agent systems, to emerging interdisciplinary areas of agent-based research.
(source: Nielsen Book Data)

• Intelligent agents and MAS applications.- Towards a Theory of Intentions for Human-Robot Collaboration.- Decentralised Control of Intelligent Devices: A Healthcare Facility Study.- Decentralised Multi-Intersection Congestion Control for Connected Autonomous Vehicles.- Congestion Management for Mobility-on-Demand Schemes that use Electric Vehicles.- Disaster Response Simulation as a Testbed for Multi-Agent Systems.- Mechanisms, Incentives, Norms, Privacy.- Rewarding miners: bankruptcy situations and pooling strategies.- A Game-Theoretical Analysis of Charging Strategies for Competing Double Auction Marketplaces.- Agents for Preserving Privacy: Learning and Decision Making Collaboratively.- Open Social Systems.- A Faithful Mechanism for Privacy-Sensitive Distributed Constraint Satisfaction Problems.- Incentivising Exploration and Recommendations for Contextual Bandits with Payments.- Emotional Agents Make a (Bank) Run.- Autonomous Agents.- An Interface for Programming Verifiable Autonomous Agents in ROS.- Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics.- Combining Levy Walks and Flocking for Cooperative Surveillance using Aerial Swarms.- Single-Agent Policies for the Multi-Agent Persistent Surveillance Problem via Artificial Heterogeneity.- Explaining the influence of prior knowledge on POMCP policies.- EUMAS 2020 Best Papers Session.- Approximating Voting Rules from Truncated Ballots.- Mechanism for Distributed Meeting Scheduling.- EUMAS-AT 2020 Joint Session.- An argumentation-based approach to generate domain-specific explanations.- Distributed multi-issue multi-lateral negotiation using a divide and rule approach.- Increasing negotiation performance at the edge of the network.- Challenges and Main Results of the Automated Negotiating Agents Competition.- Optimal Majority Rule Versus Simple Majority Rule.- Evaluating Crowdshipping Systems with Agent-Based Simulation.- Agent-based models, Social choice, Argumentation, Model-checking.- Measuring the Strength of Rhetorical Arguments.- Understanding the role of values and norms in practical reasoning.- Predicting the winners of Borda, Kemeny and Dodgson elections with supervised machine learning.- From Virtual Worlds to Mirror Worlds: A Model and Platform for Building Agent-based eXtended Realities.- Model-checking Information Diffusion in SocialNetworks with PRISM 452.- "Roads? Where we're going we don't need roads." Using Agent-based modeling to analyze the economic impact of Hyperloop introduction on a supply chain.- Sensitivity to Initial Conditions in Agent-based Models.-: Agent-oriented software engineering, Game Theory, Task Allocation, Learning.- Statecharts and Agent Technology: The Past and Future.- A Game Of Double Agents: Repeated Stackelberg Games with Role Switch.- Learning Summarised Messaging through Mediated Differentiable Inter Agent Learning.- Integrating Deep Learning and Non-monotonic Logical Reasoning for Explainable Visual Question Answering.- Multiagent Task Coordination as Task Allocation Plus Task Responsibility.- Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints.
• (source: Nielsen Book Data)

This book constitutes the revised post-conference proceedings of the 17th European Conference on Multi-Agent Systems, EUMAS 2020, and the 7th International Conference on Agreement Technologies, AT 2020, which were originally planned to be held as a joint event in Thessaloniki, Greece, in April 2020. Due to COVID-19 pandemic the conference was postponed to September 2020 and finally became a fully virtual conference. The 38 full papers presented in this volume were carefully reviewed and selected from a total of 53 submissions. The papers report on both early and mature research and cover a wide range of topics in the field of autonomous agents and multi-agent systems.
(source: Nielsen Book Data)

• Introduction What is simulation? Simulation of intelligence Criticism of simulation Swarm and the Santa Fe Institute
• Evolutionary Methods and Evolutionary Computation What is evolutionary computation? What are genetic algorithms? What is genetic programming? What is interactive evolutionary computation?
• Multi-Agent Simulation Based on Swarm Overview of Swarm Tutorial
• Evolutionary Simulation Simulation of sexual selection Swarm-based simulation of sexual selection Simulation of the prisoner's dilemma Evolving artificial creatures and artificial life
• Ant Colony-Based Simulation Collective behaviors of ants Swarm simulation of the pheromone trails of ants Ant colony optimization (ACO) Ant-clustering algorithms Swarm-based simulation of ant-clustering Ant colony-based approach to the network routing problem Ant-based job separation Emergent cooperation of army ants
• Particle Swarm Simulation Boids and flocking behaviors Simulating boids with Swarm Swarm chemistry PSO: particle swarm optimization ABC algorithm BUGS: a bug-based search strategy BUGS in Swarm
• Cellular Automata Simulation Game of life Conway class with Swarm Program that replicates itself Simulating forest fires with Swarm Segregation model simulation with Swarm Lattice gas automaton Turing model and morphogenesis simulation Simulating percolation with Swarm Silicon traffic and its control The world of Sugarscape
• Conclusion
• Appendix A: GUI Systems and Source Code Appendix B: Installing Swarm
• References
• Index
• Swarm Index
• Name Index.
• (source: Nielsen Book Data)

Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization. Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks. While the book employs the commonly used Swarm system, readers can model and develop the simulations with their own simulator. To encourage hands-on exploration of emergent systems, Swarm-based software and source codes are available for download from the author's website. A thorough overview of multi-agent simulation and supporting tools, this book shows how this type of simulation is used to acquire an understanding of complex systems and artificial life. It carefully explains how to construct a simulation program for various applications.
(source: Nielsen Book Data)

• Special Session on Agents Behaviours and Artificial Markets (ABAM).- Special Session on Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS).- Special Session on Agents and Mobile Devices (AM).- Special Sessions on Multi-Agent Systems and Ambient Intelligence (AMIRA).-m Special Sessions on Multi-Agent Systems and Ambient Intelligence (LAFL).- Special Sessions on Multi-Agent Systems and Ambient Intelligence (MASAI).- Doctoral Consortium (DC).
• (source: Nielsen Book Data)

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2016 in the special sessions: Agents Behaviours and Artificial Markets (ABAM); Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS); Agents and Mobile Devices (AM); Agent Methodologies for Intelligent Robotics Applications (AMIRA); Learning, Agents and Formal Languages (LAFLang); Multi-Agent Systems and Ambient Intelligence (MASMAI); Web Mining and Recommender systems (WebMiRes). The volume also includes the paper accepted for the Doctoral Consortium in PAAMS 2016 and Collocated Events.
(source: Nielsen Book Data)

• Chapte
• r1. Introduction to scheduling.- Chapte
• r2. Computational complexity.- Chapte
• r3. Scheduling problems with assignable due dates.- Chapte
• r4. Scheduling problems with due date assignment.- Chapte
• r5. Integrated production, inventory, and batch delivery scheduling with due date assignment.- Chapte
• r6. Scheduling problems to maximize the weighted number of JIT jobs.
• (source: Nielsen Book Data)

This book provides an introduction to the models, methods, and results of some due date-related scheduling problems in the field of multiagent scheduling. In multiagent scheduling, two or more agents share a common processing resource and each agent wants to optimize its own objective function with respect to its own set of jobs. Since the agents have conflicting objective functions, they have to negotiate among themselves with regard to sharing the common resource to optimize their own objective functions. A key feature of due date-related scheduling concerns the way in which due dates are considered: they can be given parameters or decision variables. For the former case, the motivation stems from the need to improve inventory and production management. For the latter case, due date assignment becomes a challenging issue since the decision-maker has to balance inventory holding costs against the benefits of fulfifilling orders in time. As for due dates, this book addresses the following three different scenarios: (i) The due dates of the jobs from either one or both of the two agents are decision variables, which are determined using some due date assignment models; (ii) The due dates of jobs in each job set are considered as given parameters, whereas which due date corresponds to a given job needs to determine; and (iii) The due date of each job is exogenously given. When the last case is involved, the objective function of each agent is related to the number of just-in-time jobs that are completed exactly on their due dates. For each considered scenario, depending on the model settings, and on the objective function of each agent, this book addresses the complexity, and the design of efficient exact or approximated algorithms. This book aims at introducing the author's research achievements in due date-related scheduling with two agents. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic.
(source: Nielsen Book Data)

• 1. Definition of Industrial Agents
• 2. Requirements
• 3. Methodologies and technologies
• 4. Ontologies
• 5. Service-oriented architectures and Web services
• 6. Low-level control
• 7. Simulation
• 8. Standardization
• 9. Benchmarking
• 10. Emergent topics (self-organization)
• 11. Example Applications
• 12. Vision
• 13. Road-blockers
• 14. Roadmap
• 15. Markets/ application domains.
• (source: Nielsen Book Data)

Industrial Agents explains how multi-agent systems improve collaborative networks to offer dynamic service changes, customization, improved quality and reliability, and flexible infrastructure. Learn how these platforms can offer distributed intelligent management and control functions with communication, cooperation and synchronization capabilities, and also provide for the behavior specifications of the smart components of the system. The book offers not only an introduction to industrial agents, but also clarifies and positions the vision, on-going efforts, example applications, assessment and roadmap applicable to multiple industries. This edited work is guided and co-authored by leaders of the IEEE Technical Committee on Industrial Agents who represent both academic and industry perspectives and share the latest research along with their hands-on experiences prototyping and deploying industrial agents in industrial scenarios.
(source: Nielsen Book Data)

• Introduction.- Consensus of Double-integrators over Switching Network Topology.- Generic Linear Systems over Undirected Network: Controllability and Connectivity.- Generic Linear Systems over Directed Switching Network Topology.- Extensions to Containment Control.
• (source: Nielsen Book Data)

This book aims to extend existing works on consensus of multi-agent systems systematically. The agents to be considered range from double integrators to generic linear systems. The primary goal is to explicitly characterize how agent parameters, which reflect both self-dynamics and inner coupling of each agent, and switching network topologies jointly influence the collective behaviors. A series of necessary and/or sufficient conditions for exponential consensus are derived. The contents of this book are as follows. Chapter 1 provides the background and briefly reviews the advances of consensus of multi-agent systems. Chapter 2 addresses the consensus problem of double integrators over directed switching network topologies. It is proven that exponential consensus can be secured under very mild conditions incorporating the damping gain and network topology. Chapter 3 considers generic linear systems with undirected switching network topologies. Necessary and sufficient conditions on agent parameters and connectivity of the communication graph for exponential consensus are provided. Chapter 4 furthers the study of consensus for multiple generic linear systems by considering directed switching network topologies. How agent parameters and joint connectivity work together for reaching consensus is characterized from an algebraic and geometric view. Chapter 5 extends the design and analysis methodology to containment control problem, where there exist multiple leaders. A novel analysis framework from the perspective of state transition matrix is developed. This framework relates containment to consensus and overcomes the difficulty of construction of a containment error. This book serves as a reference to the main research issues and results on consensus of multi-agent systems. Some prerequisites for reading this book include linear system theory, matrix theory, mathematics, and so on.
(source: Nielsen Book Data)

• Part 1: Computational Organisation Theory.-
• Chapter 1. Simulation Model of Financial Flows of an Industrial Enterprise.-
• Chapter 2. Drug Trafficking as Illegal Supply Chain - A Social Simulation.-
• Chapter 3. Influences of Faultlines on Organizational Performance.-
• Chapter 4. Agent-based Model and Empirical Data Integration in Architectural Design Methodology for Corporate Offices.-
• Part 2: Norms, Values and Salience: Modelling Socio-Economic Behaviour.-
• Chapter 5. Agent Based Model of Cross Media Reach of Advertising.-
• Chapter 6. Social Identity in Agent-based Models - Exploring the State of the Art.-
• Part 3: Artificial Sociality.-
• Chapter 7. Towards Fundamental Models of Radicalization.-
• Chapter 8. InCREDulity in Artificial Societies.-
• Chapter 9. How Does Social status In The Classroom Influence Classroom Community Formation? -Verification Using Multi-agent Simulation.-
• Chapter 10. Agent-based Simulation of Policies to Reconnect a City and the Countryside.
• (source: Nielsen Book Data)

This book presents the state of the art in social simulation as presented at the Social Simulation Conference 2019 in Mainz, Germany. It covers the developments in applications and methods of social simulation, addressing societal issues such as socio-ecological systems and policymaking. Methodological issues discussed include large-scale empirical calibration, model sharing and interdisciplinary research, as well as decision-making models, validation and the use of qualitative data in simulation modeling. Research areas covered include archaeology, cognitive science, economics, organization science and social simulation education. This book gives readers insight into the increasing use of social simulation in both its theoretical development and in practical applications such as policymaking whereby modeling and the behavior of complex systems is key. The book appeals to students, researchers and professionals in the various fields. .
(source: Nielsen Book Data)

• Chapter 1. Introduction.-
• Chapter 2. Network-Based Consensus of Multi-Agent Systems.-
• Chapter 3. Sampled-Data-Based Event-Triggered Consensus of Multi-Agent Systems.-
• Chapter 4. Distributed Event-triggered Secondary Control for Islanded Microgrids.-
• Chapter 5. Distributed Finite-Time Secondary Control for Islanded Microgrids.-
• Chapter 6. Distributed Resilient Finite-Time Secondary Control for Heterogeneous BESSs.-
• Chapter 7. Distributed Optimal Control of DC Microgrids with Communication Delays.-
• Chapter 8. ibuted Energy Management for Smart Grids.- Index.
• (source: Nielsen Book Data)

This book presents new techniques and methods for distributed control and optimization of networked microgrids. Distributed consensus issues under network-based and event-triggered mechanisms are first addressed in a multi-agent system framework, which can explicitly characterize the relationship between communication resources and the control performance. Then, considering the effects of network uncertainties, multi-agent system-based distributed schemes are tailored to solve the fundamental issues of networked microgrids such as distributed frequency regulation, voltage regulation, active power sharing/load sharing, and energy management. The monograph will contribute to stimulating extensive interest of researchers in electrical and control fields.
(source: Nielsen Book Data)