Autonomous Learning Systems
Autonomous Learning Systems

Author: Plamen Angelov

Publisher: John Wiley & Sons

Published: 2012-11-06

Total Pages: 259

ISBN-13: 1118481917

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Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven – there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms. Accompanied by a website hosting additional material, including the software toolbox and lecture notes. Autonomous Learning Systems provides a ‘one-stop shop’ on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.

Autonomous Learning from the Environment
Autonomous Learning from the Environment

Author: Wei-Min Shen

Publisher: Computer Science Press, Incorporated

Published: 1994

Total Pages: 355

ISBN-13: 9780716782650

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A significant contribution to the scientific foundation of autonomous learning systems, this book contains clear, up-to-date coverage of three basic subtasks: active model abstraction, model application, and integration. It is the only textbook to offer a thorough discussion of active model abstraction.

Intelligent Autonomous Systems
Intelligent Autonomous Systems

Author: Dilip Kumar Pratihar

Publisher: Springer Science & Business Media

Published: 2010-02-24

Total Pages: 269

ISBN-13: 3642116752

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This research book contains a sample of most recent research in the area of intelligent autonomous systems. The contributions include: General aspects of intelligent autonomous systems Design of intelligent autonomous robots Biped robots Robot for stair-case navigation Ensemble learning for multi-source information fusion Intelligent autonomous systems in psychiatry Condition monitoring of internal combustion engine Security management of an enterprise network High dimensional neural nets and applications This book is directed to engineers, scientists, professor and the undergraduate/postgraduate students who wish to explore this field further.

Designing Autonomous AI
Designing Autonomous AI

Author: Kence Anderson

Publisher: "O'Reilly Media, Inc."

Published: 2022-06-14

Total Pages: 253

ISBN-13: 1098110706

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Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI. This book examines: Differences between and limitations of automated, autonomous, and human decision-making Unique advantages of autonomous AI for real-time decision-making, with use cases How to design an autonomous AI from modular components and document your designs

Autonomous Learning in the Workplace
Autonomous Learning in the Workplace

Author: Jill E. Ellingson

Publisher: Taylor & Francis

Published: 2017-03-27

Total Pages: 359

ISBN-13: 1317378261

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Traditionally, organizations and researchers have focused on learning that occurs through formal training and development programs. However, the realities of today’s workplace suggest that it is difficult, if not impossible, for organizations to rely mainly on formal programs for developing human capital. This volume offers a broad-based treatment of autonomous learning to advance our understanding of learner-driven approaches and how organizations can support them. Contributors in industrial/organizational psychology, management, education, and entrepreneurship bring theoretical perspectives to help us understand autonomous learning and its consequences for individuals and organizations. Chapters consider informal learning, self-directed learning, learning from job challenges, mentoring, Massive Open Online Courses (MOOCs), organizational communities of practice, self-regulation, the role of feedback and errors, and how to capture value from autonomous learning. This book will appeal to scholars, researchers, and practitioners in psychology, management, training and development, and educational psychology.

Building Autonomous Learners
Building Autonomous Learners

Author: Woon Chia Liu

Publisher: Springer

Published: 2015-09-29

Total Pages: 311

ISBN-13: 9812876308

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This edited work presents a collection of papers on motivation research in education around the globe. Pursuing a uniquely international approach, it also features selected research studies conducted in Singapore under the auspices of the Motivation in Educational Research Lab, National Institute of Education, Singapore. A total of 15 chapters include some of the latest findings on theory and practical applications alike, prepared by internationally respected researchers in the field of motivation research in education. Each author provides his/her perspective and practical strategies on how to maximize motivation in the classroom. Individual chapters focus on theoretical and practical considerations, parental involvement, teachers’ motivation, ways to create a self-motivating classroom, use of ICT, and nurturing a passion for learning. The book will appeal to several different audiences: firstly, policymakers in education, school leaders and teachers will find it a valuable resource. Secondly, it offers a helpful guide for researchers and teacher educators in pre-service and postgraduate teacher education programmes. And thirdly, parents who want to help their children pursue lifelong learning will benefit from reading this book.

Toward Learning Robots
Toward Learning Robots

Author: Walter Van de Velde

Publisher: MIT Press

Published: 1993

Total Pages: 182

ISBN-13: 9780262720175

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The contributions in Toward Learning Robots address the question of how a robot can be designed to acquire autonomously whatever it needs to realize adequate behavior in a complex environment. In-depth discussions of issues, techniques, and experiments in machine learning focus on improving ease of programming and enhancing robustness in unpredictable and changing environments, given limitations of time and resources available to researchers. The authors show practical progress toward a useful set of abstractions and techniques to describe and automate various aspects of learning in autonomous systems. The close interaction of such a system with the world reveals opportunities for new architectures and learning scenarios and for grounding symbolic representations, though such thorny problems as noise, choice of language, abstraction level of representation, and operationality have to be faced head-on. Contents Introduction: Toward Learning Robots * Learning Reliable Manipulation Strategies without Initial Physical Models * Learning by an Autonomous Agent in the Pushing Domain * A Cost-Sensitive Machine Learning Method for the Approach and Recognize Task * A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations * Understanding Object Motion: Recognition, Learning and Spatiotemporal Reasoning * Learning How to Plan * Robo-Soar: An Integration of External Interaction, Planning, and Learning Using Soar * Foundations of Learning in Autonomous Agents * Prior Knowledge and Autonomous Learning

Autonomous Learner Model Resource Book
Autonomous Learner Model Resource Book

Author: George Betts

Publisher: Routledge

Published: 2021-09-03

Total Pages: 291

ISBN-13: 1000490246

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Autonomous Learner Model Resource Book includes activities and strategies to support the development of autonomous learners. More than 40 activities are included, all geared to the emotional, social, cognitive, and physical development of students. Teachers may use these activities and strategies with the entire class, small groups, or with individuals who are ready to be independent, self-directed, lifelong learners. These learners have the passions, abilities, skills, and attitudes to go beyond the regular curriculum and take control of their own educational pathways. Field-tested strategies and activities in the book include Find Someone Who, Teacher and Learner Questionnaires, Lifelong Notebook, Time Capsule, and Night of the Notables.