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A Unifying Framework for Formal Theories of Novelty: Discussions, Guidelines, and Examples for Artificial Intelligence de Terrance Boult

A Unifying Framework for Formal Theories of Novelty: Discussions, Guidelines, and Examples for Artificial Intelligence: Synthesis Lectures on Computer Vision

Editat de Terrance Boult, Walter Scheirer
en Limba Engleză Hardback – 2 aug 2023
This book presents the first unified formalization for defining novelty across the span of machine learning, symbolic-reasoning, and control and planning-based systems.  Dealing with novelty, things not previously seen by a system, is a critical issue for building vision-systems and general intelligent systems.  The book presents examples of using this framework to define and evaluate in multiple domains including  image recognition image-based open world learning,  hand-writing and author analysis,  CartPole Control,  Image Captioning, and Monopoly.  Chapters are written by well-known contributors to this new and emerging field.  In addition, examples are provided from multiple areas, such as machine-learning based control problems, symbolic reasoning, and multi-player games.  




                
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Specificații

ISBN-13: 9783031330537
ISBN-10: 3031330536
Pagini: 110
Ilustrații: XV, 110 p. 35 illus., 33 illus. in color.
Dimensiuni: 168 x 240 mm
Greutate: 0.34 kg
Ediția:1st ed. 2024
Editura: Springer Nature Switzerland
Colecția Springer
Seria Synthesis Lectures on Computer Vision

Locul publicării:Cham, Switzerland

Cuprins

A Unifying Framework for Novelty.- Example Application of Framework: CartPole Domain.- Image Classification.- Novelty in Handwriting Recognition.- Visual Media Caption Domain.- Monopoly Multi-Agent Game Domain.- Related Work.- Prior Perspectives on Novelty and Conclusions.

Notă biografică

Terrance E. Boult, Ph.D., is a Distinguished Professor and El Pomar Endowed Professor of Innovation and Security in the Department of Computer Science at the University of Colorado at Colorado Springs.  He is also an IEEE Fellow and an internationally acknowledged researcher in machine learning, computer vision, biometrics, and cybersecurity with 15 patents issued and 400+ articles.  Dr. Boult received the B.S. degree in Applied Mathematics, the M.S. degree in Computer Science, and the Ph.D. degree in Computer Science from Columbia University.  He has won multiple teaching awards, research/innovation awards, best paper awards, best reviewer awards, and IEEE service awards. 

Walter Scheirer, Ph.D., is Dennis O. Doughty Collegiate Associate Professor in the Department of Computer Science and Engineering at the University of Notre Dame.  Previously, he was a postdoctoral fellow at Harvard University with affiliations in the Schoolof Engineering and Applied Sciences, Department of Molecular and Cellular Biology, and Center for Brain Science.  Dr. Scheirer received his Ph.D. from the University of Colorado and his M.S. and B.A. degrees from Lehigh University. He has extensive experience in the areas of computer vision, machine learning, and image processing.  His current research is focused on media forensics and studying disinformation circulating on social media.

Textul de pe ultima copertă

This book presents the first unified formalization for defining novelty across the span of machine learning, symbolic-reasoning, and control and planning-based systems.  Dealing with novelty, things not previously seen by a system, is a critical issue for building vision-systems and general intelligent systems.  The book presents examples of using this framework to define and evaluate in multiple domains including  image recognition image-based open world learning,  hand-writing and author analysis,  CartPole Control,  Image Captioning, and Monopoly.  Chapters are written by well-known contributors to this new and emerging field.  In addition, examples are provided from multiple areas, such as machine-learning based control problems, symbolic reasoning, and multi-player games. 

Caracteristici

Addresses novelty to enhance building both vision-systems and planning-based systems Establishes formal definitions of novelty subtypes that will help produce a consistent lexicon for researchers Includes examples of applications, including image captioning, multi-agent game domain, and handwriting recognition