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This book draws inspiration from natural shepherding, whereby a farmer utilizes sheepdogs to herd sheep, to inspire a scalable and inherently human friendly approach to swarm control. The book discusses advanced artificial intelligence (AI) approaches needed to design smart robotic shepherding agents capable of controlling biological swarms or robotic swarms of unmanned vehicles. These smart shepherding agents are described with the techniques applicable to the control of Unmanned X Vehicles (UxVs) including air (unmanned aerial vehicles or UAVs), ground (unmanned ground vehicles or UGVs), underwater (unmanned underwater vehicles or UUVs), and on the surface of water (unmanned surface vehicles or USVs). This book proposes how smart ‘shepherds'could be designed and used to guide a swarm of UxVs to achieve a goal while ameliorating typical communication bandwidth issues that arise in the control of multi agent systems. The book covers a wide range of topics ranging from the design ofdeep reinforcement learning models for shepherding a swarm, transparency in swarm guidance, and ontology-guided learning, to the design of smart swarm guidance methods for shepherding with UGVs and UAVs. The book extends the discussion to human-swarm teaming by looking into the real-time analysis of human data during human-swarm interaction, the concept of trust for human-swarm teaming, and the design of activity recognition systems for shepherding.Presents a comprehensive look at human-swarm teaming;Tackles artificial intelligence techniques for swarm guidance;Provides artificial intelligence techniques for real-time human performance analysis.

An authoritative guide to computer simulation grounded in a multi-disciplinary approach for solving complex problems Simulation and Computational Red Teaming for Problem Solving offers a review of computer simulation that is grounded in a multi-disciplinary approach. The authors present the theoretical foundations of simulation and modeling paradigms from the perspective of an analyst. The book provides the fundamental background information needed for designing and developing consistent and useful simulations. In addition to this basic information, the authors explore several advanced topics. The book's advanced topics demonstrate how modern artificial intelligence and computational intelligence concepts and techniques can be combined with various simulation paradigms for solving complex and critical problems. Authors examine the concept of Computational Red Teaming to reveal how the combined fundamentals and advanced techniques are used successfully for solving and testing complex real-world problems. This important book: • Demonstrates how computer simulation and Computational Red Teaming support each other for solving complex problems • Describes the main approaches to modeling real-world phenomena and embedding these models into computer simulations • Explores how a number of advanced artificial intelligence and computational intelligence concepts are used in conjunction with the fundamental aspects of simulation Written for researchers and students in the computational modelling and data analysis fields, Simulation and Computational Red Teaming for Problem Solving covers the foundation and the standard elements of the process of building a simulation and explores the simulation topic with a modern research approach.

This book introduces the linkage between evolutionary computation and complex networks and the advantages of cross-fertilising ideas from both fields. Instead of introducing each field individually, the authors focus on the research that sits at the interface of both fields. The book is structured to address two questions: (1) how complex networks are used to analyze and improve the performance of evolutionary computation methods? (2) how evolutionary computation methods are used to solve problems in complex networks? The authors interweave complex networks and evolutionary computing, using evolutionary computation to discover community structure, while also using network analysis techniques to analyze the performance of evolutionary algorithms. The book is suitable for both beginners and senior researchers in the fields of evolutionary computation and complex networks.

This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness. Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume. The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems. The book augments theory with real-world applications including cyber security, defence and space.

Offering a concise and multidisciplinary reference guide to the state of the art in Australian operations research, this book will be of great value to academics working in many disciplines associated with operations research, as well as industrial practitioners engaged in planning, scheduling and logistics.Over 60 papers, with topics ranging from academic research techniques and case studies to industrial and administrative best practices in operations research, address aspects such as:• optimization, combinatorial optimization, decision analysis, supply-chain management, queuing and routing, and project management; and• logistics, government, cyber security, health-care systems, mining and material processing, ergonomics and human factors, space applications, telecommunications and transportation, among many others.This book presents the Proceedings of the National Conference of the Australian Society for Operations Research, the premier professional organization for Australian academics and practitioners working in optimization and other disciplines related to operations research. The conference was held in Canberra in November 2016.

This volume is an initiative undertaken by the IEEE Computational Intelligence Society's Task Force on Security, Surveillance and Defense to consolidate and disseminate the role of CI techniques in the design, development and deployment of security and defense solutions. Applications range from the detection of buried explosive hazards in a battlefield to the control of unmanned underwater vehicles, the delivery of superior video analytics for protecting critical infrastructures or the development of stronger intrusion detection systems and the design of military surveillance networks. Defense scientists, industry experts, academicians and practitioners alike will all benefit from the wide spectrum of successful applications compiled in this volume. Senior undergraduate or graduate students may also discover uncharted territory for their own research endeavors.

Written to bridge the information needs of management and computational scientists, this book presents the first comprehensive treatment of Computational Red Teaming (CRT). The author describes an analytics environment that blends human reasoning and computational modeling to design risk-aware and evidence-based smart decision making systems. He presents the Shadow CRT Machine, which shadows the operations of an actual system to think with decision makers, challenge threats, and design remedies. This is the first book to generalize red teaming (RT) outside the military and security domains and it offers coverage of RT principles, practical and ethical guidelines. The author utilizes Gilbert's principles for introducing a science. Simplicity: where the book follows a special style to make it accessible to a wide range of readers. Coherence: where only necessary elements from experimentation, optimization, simulation, data mining, big data, cognitive information processing, and system thinking are blended together systematically to present CRT as the science of Risk Analytics and Challenge Analytics. Utility: where the author draws on a wide range of examples, ranging from job interviews to Cyber operations, before presenting three case studies from air traffic control technologies, human behavior, and complex socio-technical systems involving real-time mining and integration of human brain data in the decision making environment.

The aim of the book is to lay out the foundations and provide a detailed treatment of the subject. It will focus on two main elements in dual phase evolution: the relationship between dual phase evolution and other phase transition phenomena and the advantages of dual phase evolution in evolutionary computation and complex adaptive systems. The book will provide a coherent picture of dual phase evolution that encompasses these two elements and frameworks, methods and techniques to use this concept for problem solving.

This state-of-the-art survey offers a renewed and refreshing focus on the progress in evolutionary computation, in neural networks, and in fuzzy systems. The book presents the expertise and experiences of leading researchers spanning a diverse spectrum of computational intelligence in these areas. The result is a balanced contribution to the research area of computational intelligence that should serve the community not only as a survey and a reference, but also as an inspiration for the future advancement of the state of the art of the field. The 13 selected chapters originate from lectures and presentations given at the IEEE World Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.

This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007. The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems.

Artificial life is now a recognized discipline of research with many important applications and software tools. However, many theoretical issues remain unresolved. This book brings together a cross-section of key developments in artificial life, which in turn gives us new insight into the theory of complex systems.The central ideas of the book surround genetics and evolution in an artificial life framework. Topics covered include maintenance of genetic diversity, hierarchical structures and stability of ecosystems. Underpinning these topics are key theoretical developments surrounding network complexity, the development of pattern languages for complex networks and a deeper understanding of the edge of chaos where complex systems live. Practical applications include optimization, gene regulatory networks, modeling the spread of disease and the evolution of ageing.The reader will gain an insight into the mathematical techniques at the core of artificial life and encounter a sufficient diversity of applications to stimulate new directions in their own field.