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Format: 1 online resource (185 pages)

Edition: 1st ed.

ISBN: 9783031614187

Series Statement: Lecture Notes in Networks and Systems Series ; v.1009

Note: Intro -- Preface -- Organization -- Contents -- Algorithmic Foundations of Reinforcement Learning -- 1 Introduction -- 2 Reinforcement Learning Fundamentals -- 2.1 Markov Decision Processes (MDPs) -- 2.2 Solving an MDP: Dynamic Programming -- 2.3 Model-Free Prediction -- 3 Reinforcement Learning Algorithms -- 3.1 Model-Free Reinforcement Learning -- 3.2 Deep Q-Learning -- 3.3 Policy Methods -- 3.4 Actor-Critic Methods -- 3.5 Off-Policy Deep RL and Continuous Control -- 3.6 Conclusion and Summary -- References -- Autonomous Emergency Landing of an Aircraft in Case of Total Engine-Out -- 1 Introduction -- 2 High-Key/Low-Key Heuristics -- 3 Computer-Based Avionics Systems -- 4 Optimized Emergency Approach Routes that Provide Altitude Division -- 4.1 General Problem Analysis -- 4.2 Compensation of Wind Influence -- 4.3 Database of Emergency Landing Fields -- 4.4 Safe2Land App (Version 1) -- 4.5 Safe2Land App (Version 2) -- 5 Conclusion -- References -- The Safety-Related Real-Time Language SafePEARL -- 1 Introduction -- 2 Major Language Properties -- 2.1 Program Structure -- 2.2 Algorithmics -- 2.3 Process Data Input and Output -- 2.4 Real-Time Programming -- 3 PEARL for Distributed Systems -- 4 Safety-Related PEARL -- 4.1 Verification-Oriented Language Subsets -- 4.2 SIL1: Constructive Preclusion of Error Sources -- 4.3 SIL2: Predictable Time Behaviour -- 4.4 SIL3: Function Block Diagrams -- 4.5 SIL4: Cause-Effect Tables -- 4.6 Synopsis of the Language Subsets -- 5 Safe Sequential Function Charts -- 6 PEARL as Specification Language -- References -- Meta-Learning for Time Series Analysis and/or Forecasting: Concept Review and Comprehensive Critical Comparative Survey -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Problem Statement and Research Questions -- 2 Fundamentals of Meta-Learning -- 2.1 Fundamentals Concepts of Meta-Learning. , 2.2 Meta-Learning for Time Series Data: Classification Problems vs. Regression Problems -- 2.3 Discussion of the Different Meta-Learning Taxonomies and Approaches -- 2.4 Critical Discussion of the Meta-Learning Taxonomies and Approaches with Focusing on Time Series Data -- 3 Comprehensive Review of Meta-Learning in TSA/TSF with Regard to ZSL, OSL and FSL (RQ2) -- 3.1 Relevance of Meta-Learning in TSA and TSF -- 3.2 Critical Discussion of ZSL, OSL and FSL in Meta-Learning -- 3.3 Meta-Learning Example Formulation of ZSL, OSL and FSL for TSA and TSF -- 3.4 Pipeline Discussion for ZSL, OSL and FSL for TSF -- 3.5 Comprehensive Review of Meta-Learning with Regard to ZSL, OSL and FSL for TSA/TSF -- 4 Illustrative Implementations of ZSL, OSL and FSL for TSF (RQ3) -- 4.1 Dataset Presentation -- 4.2 Setup of the Implementation -- 4.3 Discussion of Performance Metrics of Relevance to ZSL/OSL/FSL -- 4.4 ML Baseline Implementation: Standard Encoder -- 4.5 ZSL Forecasting Implementation: ForecastPFN -- 4.6 OSL Forecasting Implementation: Siamese Networks -- 4.7 FSL Forecasting Implementation: Reptile -- 4.8 Comparative Discussion of the Results -- 5 Challenges and Limitations of Current Meta-Learning Techniques and Directions for Future Research in the Context of TSF/TSA -- 5.1 Challenges and Limitations of Meta-Learning for TSA/TSF -- 5.2 Suggesting Potential Avenues and Directions for Future Research in TSA/TSF -- 6 Conclusion -- References -- Ensemble Learning with Physics-Informed Neural Networks for Harsh Time Series Analysis -- 1 Introduction -- 2 Physics-Informed Neural Networks (PINN): Mechanics, Recent Extensions, and Potentials -- 2.1 Basics of Physics-Informed Neural Networks -- 2.2 Recent Extensions of PINN -- 2.3 Potentials of PINN in Solving Complex Problems. , 3 Challenges in Analyzing and Forecasting Complex and Multivariate Harsh Time Series: Focusing on Traffic and Weather -- 3.1 Challenges in Traditional Analysis of Time Series -- 3.2 Challenges in Forecasting Time Series -- 3.3 Special Considerations for Traffic and Weather Time Series -- 3.4 The Need for Advanced Approaches -- 4 Technical Review of Advanced Physics-Informed Neural Networks for Reliable Analysis and Forecasting of Complex Harsh Time Series in Traffic and Weather -- 4.1 Advanced Physics-Informed Neural Networks -- 4.2 Classification and Anomaly Detection in Traffic Data -- 4.3 Forecasting in Traffic and Weather -- 4.4 Performance Comparison with Traditional Approaches -- 5 Ensemble Learning for Enhanced Performance of Physics-Informed Neural Networks in Complex Time Series Analysis and Forecasting -- 5.1 Ensemble Learning Techniques and PINN -- 5.2 Ensemble Architectures with Selected DL Models -- 6 Enhancing Explainability and Interpretability in Time Series Analysis and Forecasting by Physics-Informed Neural Networks -- 6.1 Explainability in Time Series Analysis -- 6.2 Interpretability in Time Series Forecastings -- 6.3 Case Studies: Examples of Explainable and Interpretable Outcomes -- 6.4 Bridging the Gap Between Data-Driven and Physics-Informed Models -- 6.5 Challenges and Future Directions -- 7 Advantages of Using PINN for Traffic and Weather Data -- 8 Future Work -- References -- Language Meets Vision: A Critical Survey on Cutting-Edge Prompt-Based Image Generation Models -- 1 Introduction -- 2 Description of the Concept of Language-Driven Image Generation Models -- 2.1 Background and Evolution of Prompt-Based Image Generation -- 2.2 Motivation and Expectations of Prompt-Based Image Generation -- 3 Comprehensive Specification Book for Prompt-Based Generative Models -- 3.1 User Input (Natural Language Prompts) Guidelines. , 3.2 Training Data -- 3.3 Model Capabilities -- 3.4 Output Evaluation -- 3.5 Training Pipeline -- 4 Historical Overview Review of Deep Learning in Image Generation -- 5 Critical Comparative Analysis of the State-of-the-Art Prompt-Based Image Generation Models -- 5.1 Review of the State-of-the-Art Models -- 5.2 Comparative Evaluation -- 5.3 Gap Analysis: Identifying Limitations -- 6 Training Techniques for Generative Models -- 6.1 Challenges in Training -- 7 Conclusion and Future Work -- References -- Blockchain and Beyond - A Survey on Scalability Issues -- 1 Introduction -- 2 Brief History of Blockchain -- 2.1 Bitcoin - A (Partial) Success Story -- 3 Optimizations Regarding Scalability -- 3.1 Approaches to Mitigate the Scalability Limitations of Traditional Blockchains -- 3.2 Blockchain Trilemma -- 3.3 Layer 2 Solutions -- 3.4 Payment Channel -- 3.5 Side Chains -- 3.6 Optimistic Rollup -- 4 Directed Acyclic Graph as DLT -- 4.1 Non-linear Structure -- 4.2 On-tangle Voting -- 4.3 Conflict Resolution -- 4.4 Side Effects and Their Treatment -- 4.5 Bait-and-Switch Attack -- 5 Conclusion -- References -- Emotion-Aware Chatbots: Understanding, Reacting and Adapting to Human Emotions in Text Conversations -- 1 Introduction -- 1.1 Chatbots -- 1.2 The Case for Self-adapting Emotionally Intelligent Chatbots -- 2 Emotion in AI -- 2.1 Overview -- 2.2 Learning Based on Emotion -- 3 Methodology -- 3.1 Search Method -- 3.2 Categorization of Approaches -- 4 Results -- 4.1 Adaption of Agent's Emotion Based on User's Emotion -- 4.2 Response Selection Based on User's Emotion -- 4.3 Learning Based on Emotion -- 5 Conclusion -- References -- Author Index.

Additional Edition: Print version: Unger, Herwig Advances in Real-Time and Autonomous Systems Cham : Springer International Publishing AG,c2024 ISBN 9783031614170

Language: English