Reinforcement and Systemic Machine Learning for Decision Making

Description

Reinforcement and Systemic Machine Learning for Decision MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available?or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm?creating new learning applications and, ultimately, more intelligent machines.The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making.Chapters include:Introduction to Reinforcement and Systemic Machine LearningFundamentals of Whole-System, Systemic, and Multiperspective Machine LearningSystemic Machine Learning and ModelInference and Information IntegrationAdaptive LearningIncremental Learning and Knowledge RepresentationKnowledge Augmentation: A Machine Learning PerspectiveBuilding a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.Content: Chapter 1 Introduction to Reinforcement and Systemic Machine Learning (pages 1–21): Chapter 2 Fundamentals of Whole?System, Systemic, and Multiperspective Machine Learning (pages 23–56): Chapter 3 Reinforcement Learning (pages 57–76): Chapter 4 Systemic Machine Learning and Model (pages 77–98): Chapter 5 Inference and Information Integration (pages 99–118): Chapter 6 Adaptive Learning (pages 119–149): Chapter 7 Multiperspective and Whole?System Learning (pages 151–175): Chapter 8 Incremental Learning and Knowledge Representation (pages 177–208): Chapter 9 Knowledge Augmentation: A Machine Learning Perspective (pages 209–236): Chapter 10 Building a Learning System (pages 237–260):