دانلود کتاب Shepherding UxVs for Human-Swarm Teaming: An Artificial Intelligence Approach to Unmanned X Vehicles (Unmanned System Technologies)

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Foreword Preface Acknowledgements Contents Contributors Generalised Shepherding Notations 1 Smart Shepherding: Towards Transparent Artificial Intelligence Enabled Human-Swarm Teams 1.1 From Swarm Intelligence to Shepherding 1.2 Shepherding 1.3 The Practical Significance of Shepherding 1.4 Reactive vs Cognitive Shepherds and Sheepdogs 1.5 Swarm Ontology for Transparent Artificial Shepherding 1.6 Artificial Intelligence Architecture for Shepherds and Sheepdogs 1.6.1 Shepherds and Sheepdogs Autonomy Architecture 1.6.2 Shepherds and Sheepdogs Contextual Awareness Architecture 1.6.3 Smart Shepherds and Sheepdogs Overall Architecture 1.7 Conclusion References Part I Shepherding Simulation 2 Shepherding Autonomous Goal-Focused Swarms in Unknown Environments Using Hilbert Space-Filling Paths 2.1 Introduction 2.2 Background Research 2.3 Methodology 2.3.1 Simulation Setup 2.3.2 Force Modulation 2.3.3 Path Planning 2.3.4 Hilbert Space-Filling Curves 2.4 Results and Discussion 2.4.1 Force Weights 2.4.2 Number of Goals 2.5 Conclusion References 3 Simulating Single and Multiple Sheepdogs Guidance of a Sheep Swarm 3.1 Introduction 3.2 Experimental and Computational Details 3.2.1 Problem Formulation 3.2.2 Sheep Agent Model 3.2.3 Shepherd Agent Model 3.2.4 Swarm Guidance Algorithm Design 3.2.5 Experimental Design 3.3 Simulation Results 3.3.1 Herding with a Single Shepherd 3.3.2 Herding with a Multi-Shepherd Swarm 3.3.3 Herding with a Multi-Shepherd Swarm Plus Formation 3.3.4 Analysis of the Shepherding Task as a Function of Guidance Scheme 3.4 Conclusions References 4 The Influence of Stall Distance on Effective Shepherdingof a Swarm 4.1 Introduction 4.2 Background 4.2.1 Driving Interactions 4.2.2 Collecting Interactions 4.3 Methodology 4.4 Experimental Design 4.4.1 Genetic Algorithm Exploration of Stall Distance 4.4.2 Systematic Analysis of Stall Distance 4.5 Results 4.5.1 Results of Genetic Algorithm Exploration of Stall Distance 4.5.2 Systematic Analysis of Stall Distance 4.5.2.1 Success Rates for Herding 4.5.2.2 Herding Time Steps and Distances 4.6 Conclusion References Part II Learning and Optimisation for Shepherding 5 Mission Planning for Shepherding a Swarm of Uninhabited Aerial Vehicles 5.1 Introduction 5.2 Overview of Mission Planning for Shepherding UAV Swarm 5.3 Task Planning 5.3.1 Task Decomposition 5.3.2 Task Assignment 5.3.3 Algorithms for Task Planning 5.4 Motion Planning 5.4.1 Path Planning 5.4.2 Trajectory Planning 5.4.3 Algorithms for Motion Planning 5.5 Mission Planning 5.6 Conclusion and Discussion References 6 Towards Ontology-Guided Learning for Shepherding 6.1 Introduction 6.2 Learning Shepherding Systems 6.3 Prior Knowledge in Learning Systems 6.4 Hybrid Learning 6.4.1 Guided Learning Systems 6.5 Ontology Guided Shepherding 6.6 Future Work 6.7 Conclusion References 7 Activity Recognition for Shepherding 7.1 Introduction 7.1.1 Problem Frame 7.1.2 Motivation 7.2 Activity Recognition 7.2.1 Elements of Activity Recognition 7.2.1.1 Agent 7.2.1.2 Agent Types 7.2.1.3 Action and Activity 7.2.1.4 Defining Activity Recognition 7.2.2 Problem Components 7.2.2.1 Agent Design 7.2.3 Approaches 7.2.3.1 Data-Driven Approaches 7.2.3.2 Knowledge-Driven Approaches 7.2.3.3 Hybrid Approaches 7.3 Shepherding 7.3.1 Open Challenges 7.3.1.1 Activity Verification 7.3.1.2 Adversarial Activity Recognition 7.3.1.3 Context-Aware Activity Recognition 7.3.1.4 Cross-Domain (Multi-Modality) Activity Recognition 7.3.1.5 Dynamic Activity Recognition 7.3.1.6 Inter- and Intra-Activity Delay and Task Selection 7.3.2 Solving the Activity Recognition for Shepherding Problem 7.3.2.1 Shepherding Taxonomy 7.3.2.2 Framework 7.3.2.3 Central Challenge 7.4 Formulating Activity Recognition for Shepherding 7.4.1 Describing Shepherding Behaviours 7.4.2 Classifying Behaviour Through Spatial Data 7.4.2.1 Methodology 7.4.2.2 Analysis 7.5 Conclusion References 8 Stable Belief Estimation in Shepherd-Assisted Swarm Collective Decision Making 8.1 Introduction 8.2 Related Work 8.3 Problem Definition and Assumptions 8.4 Shepherd-Assisted Algorithm 8.4.1 Swarm Members' Behaviour 8.4.2 Shepherd's Behaviour 8.5 Experimental Results 8.6 Discussion 8.7 Conclusion and Future Directions References Part III Sky Shepherding 9 Sky Shepherds: A Tale of a UAV and Sheep 9.1 Introduction 9.2 Shepherding Models 9.3 Flock Dynamics 9.4 Autonomous Sky Shepherd Methodology 9.5 Concluding Comments References 10 Apprenticeship Bootstrapping Reinforcement Learning for Sky Shepherding of a Ground Swarm in Gazebo 10.1 Introduction 10.2 Aerial/Sky Shepherding of Ground Swarm 10.2.1 Unmanned Air–Ground Vehicles Coordination 10.2.2 A Brief Review of Coordination in Unmanned Air–Ground Vehicles 10.2.3 Autonomous Aerial/Sky Shepherding in Air–Ground Coordination 10.3 The Aerial/Sky Shepherding Task 10.3.1 Description of the Aerial/Sky Shepherding Task 10.3.2 The Aerial/Sky Shepherding Task as a Multi-Agent System 10.4 Learning Approaches 10.4.1 Reinforcement Learning 10.4.1.1 Markov Decision Process 10.4.1.2 Q-Learning 10.4.1.3 Deep Q-Network 10.4.1.4 Multi-Agent Reinforcement Learning 10.4.2 Apprenticeship Learning 10.4.2.1 Supervised Learning 10.4.2.2 Inverse Reinforcement Learning 10.4.2.3 Hybrid Methods 10.4.2.4 Multi-Agent Apprenticeship Learning/Imitation Learning 10.4.3 Apprenticeship Bootstrapping 10.4.3.1 Clarifying Tasks 10.4.3.2 Learning Tasks 10.4.3.3 Apprenticeship Bootstrapping Approach 10.5 Initial Results 10.5.1 Proposed Methodology 10.5.1.1 Evaluation Metrics 10.5.2 Experimental Design 10.5.2.1 Demonstration Interface 10.5.2.2 Actions and States Space 10.5.2.3 Experimental Setups 10.5.3 Results and Discussion 10.5.3.1 Training 10.5.3.2 Testing 10.6 Conclusions and Open Issues References 11 Logical Shepherd Assisting Air Traffic Controllers for Swarm UAV Traffic Control Systems 11.1 Introduction 11.2 Background 11.2.1 Advantages of Shepherding in Air Traffic Control 11.2.2 Challenges for Shepherding in Air Traffic Control 11.3 Asynchronous Shepherding 11.4 The Digital Twin 11.4.1 ATOMS 11.4.2 UTC Interface 11.4.3 Applying the Asynchronous Shepherding Rules 11.4.4 Asynchronous Shepherding Algorithm 11.4.5 Issues for Future Research 11.5 Conclusion References Part IV Human-Shepherding Integration 12 Transparent Shepherding: A Rule-Based Learning Shepherd for Human Swarm Teaming 12.1 Introduction 12.2 Challenges for Efficient Human Swarm Teaming 12.3 Fundamentals of Rule-Based Artificial Intelligence 12.3.1 Structure 12.3.2 Representation of Knowledge: Rules 12.3.3 The Inference Mechanism 12.4 Learning Classifier Systems 12.4.1 Learning Classifier System Components 12.5 Learning Classifier Systems for Human Swarm Teaming 12.5.1 Transparency: Rules as Justifiers 12.5.2 Flexibility 12.5.3 Multi-Agent Coordination 12.6 Learning Classifier System Model for Shepherding 12.6.1 Sheep-Dog Herding Problem 12.6.2 XCS Classifier Representation 12.6.3 Experimental Setup 12.6.4 Results 12.7 Summary and Future Work References 13 Human Performance Operating Picture for Shepherding a Swarm of Autonomous Vehicles 13.1 Introduction 13.2 Human Performance 13.3 Performance Measures in Human-Swarm Teams 13.3.1 Task- and System-Related Measures 13.3.2 Human-Related Measures 13.3.3 Workload 13.3.4 Situation Awareness 13.3.5 Trust 13.4 Human Performance Operating Picture for Swarm Shepherding 13.4.1 Design Considerations 13.4.2 Elements of H-FOP Design 13.4.3 Shepherding a Group of Robots for Effective Human-Swarm Teaming 13.5 Conclusions References Index