Artificial intelligence in wireless robotics:

Robots, autonomous vehicles, unmanned aerial vehicles, and smart factories will significantly change human living style in digital society. Artificial Intelligence in Wireless Roboticsintroduces how wireless communications and networking technology enhances facilitation of artificial intelligence in...

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Bibliographische Detailangaben
1. Verfasser: Chen, Kwang-Cheng (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Gistrup, Dänemark River Publishers [2020]
Schriftenreihe:River publishers series in information science and technology
Schlagworte:
Online-Zugang:FHN01
https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6406229
Zusammenfassung:Robots, autonomous vehicles, unmanned aerial vehicles, and smart factories will significantly change human living style in digital society. Artificial Intelligence in Wireless Roboticsintroduces how wireless communications and networking technology enhances facilitation of artificial intelligence in robotics, which bridges basic multi-disciplinary knowledge among artificial intelligence, wireless communications, computing, and control in robotics. A unique aspect of the book is to introduce applying communication and signal processing techniques to enhance traditional artificial intelligence in robotics and multi-agent systems
Front Cover -- Artificial Intelligence in Wireless Robotics -- Contents -- Preface -- List of Figures -- List of Tables -- List of Abbreviations -- 01 Introduction to Artificial Intelligence and Robotics -- 1.1 Common Sense Knowledge of AI, Cybernetics, and Robotics -- 1.2 Intelligent Agents -- 1.2.1 The Concept of Rationality -- 1.2.2 System Dynamics -- 1.2.3 Task Environments -- 1.2.4 Robotics and Multi-Agent Systems -- 1.3 Reasoning -- 1.3.1 Constraint Satisfaction Problems -- 1.3.2 Solving CSP by Search -- References -- 02 Basic Search Algorithms -- 2.1 Problem-Solving Agents -- 2.2 Searching for Solutions -- 2.3 Uniform Search -- 2.3.1 Breadth-First Search -- 2.3.2 Dynamic Programming -- 2.3.3 Depth-first Search -- 2.4 Informed Search -- 2.5 Optimization -- 2.5.1 Linear Programming -- 2.5.2 Nonlinear Programming -- 2.5.3 Convex Optimization -- References -- 03 Machine Learning Basics -- 3.1 Supervised Learning -- 3.1.1 Regression -- 3.1.2 Bayesian Classification -- 3.1.3 KNN -- 3.1.4 Support Vector Machine -- 3.2 Unsupervised Learning -- 3.2.1 K-Means Clustering -- 3.2.2 EM Algorithms -- 3.2.3 Principal Component Analysis -- 3.3 Deep Neural Networks -- 3.4 Data Preprocessing -- References -- 04 Markov Decision Processes -- 4.1 Statistical Decisions -- 4.1.1 Mathematical Foundation -- 4.1.2 Bayes Decision -- 4.1.3 Radar Signal Detection -- 4.1.4 Bayesian Sequential Decision -- 4.2 Markov Decision Processes -- 4.2.1 Mathematical Formulation of Markov Decision Process -- 4.2.2 Optimal Policies -- 4.2.3 Developing Solutions to Bellman Equation -- 4.3 Decision Making and Planning: Dynamic Programming -- 4.4 Application of MDP to Search A Mobile Target -- 4.5 Multi-Armed Bandit Problem -- 4.5.1 ε-Greedy Algorithm -- 4.5.2 Upper Confidence Bounds -- 4.5.3 Thompson Sampling -- References -- 05 Reinforcement Learning
Beschreibung:Description based on publisher supplied metadata and other sources
Beschreibung:1 Online-Ressource (xxx, 323 Seiten) Illustrationen
ISBN:9788770221177

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