Reinforcement and Systemic Machine Learning for Decision Making / IEEE Series on Systems Science and Engineering (ePub)

Reinforcement and Systemic Machine Learning for Decision
Making

There 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
Learning

* Fundamentals of Whole-System, Systemic, and Multiperspective
Machine Learning

* Systemic Machine Learning and Model

* Inference and Information Integration

* Adaptive Learning

* Incremental Learning and Knowledge Representation

* Knowledge Augmentation: A Machine Learning Perspective

* Building 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.