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Feature Models: AI-Driven Design, Analysis and Applications: SpringerBriefs in Computer Science

Autor Alexander Felfernig, Andreas Falkner, David Benavides
en Limba Engleză Paperback – 30 iun 2024
This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.
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Specificații

ISBN-13: 9783031618734
ISBN-10: 3031618734
Pagini: 110
Ilustrații: X, 122 p.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.22 kg
Ediția:2024
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Preface .- 1) Introduction .- 2) Feature Modelling .- 3) Analysis of Feature Models .- 4) Interacting with Feature Model Configurators .- 5) Tools and Applications .

Notă biografică

Alexander Felfernig is Full Professor at the Graz University of Technology. Together with his colleagues, he focuses on various research areas including recommender systems, knowledge-based configuration, software product lines, model-based diagnosis, and machine learning. Specifically, his research revolves around the utilization of recommender systems and machine learning within configuration and product line contexts, aligning closely with the central theme of the book.
Andreas Falkner is the Principal Key Expert for Configuration & Planning at Siemens' technology field of Data Analytics and Artificial Intelligence. Since 1992 he has been developing product configurators for technical systems of various Siemens divisions. Currently he is involved in projects aiming at improving configuration processes and tools, especially by applying data-driven and generative AI and integrating sustainability metrics over the whole product life cycle.
David Benavides is Full Professor of Software Engineering and leads the Diverso Lab at the University of Seville. He is in the direction board of UVL (Universal Variability Language, a community effort towards a unified language for variability models), UVLHUb (an open science repository for feature models written in UVL)  and flama (a variability analysis tool written in Python).  His main research interests include software product lines, feature modelling, variability-intensive systems, computational thinking and libre and open-source software development.

Textul de pe ultima copertă

This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.

Caracteristici

This book is open access, which means that you have free and unlimited access Provides a basic introduction to feature modelling and analysis for researchers and practitioners Covers feature modelling languages, feature model analysis, and interacting with feature model configurators Emphasizes the integration of AI methods in the feature modelling process