Hybrid Offline/Online Methods for Optimization Under Uncertainty

Balancing the solution-quality/time trade-off and optimizing problems which feature offline and online phases can deliver significant improvements in efficiency and budget control. Offline/online integration yields benefits by achieving high quality solutions while reducing online computation time.

This book considers multi-stage optimization problems under uncertainty and proposes various methods that have broad applicability. Due to the complexity of the task, the most popular approaches depend on the temporal granularity of the decisions to be made and are, in general, sampling-based methods and heuristics. Long-term strategic decisions that may have a major impact are typically solved using these more accurate, but expensive, sampling-based approaches. Short-term operational decisions often need to be made over multiple steps within a short time frame and are commonly addressed via polynomial-time heuristics, with the more advanced sampling-based methods only being applicable if their computational cost can be carefully managed. Despite being strongly interconnected, these 2 phases are typically solved in isolation.

In the first part of the book, general methods based on a tighter integration between the two phases are proposed and their applicability explored, and these may lead to significant improvements. The second part of the book focuses on how to manage the cost/quality trade-off of online stochastic anticipatory algorithms, taking advantage of some offline information.

All the methods proposed here provide multiple options to balance the quality/time trade-off in optimization problems that involve offline and online phases, and are suitable for a variety of practical application scenarios.

Author:De Filippo, A.
Pages: 124
Binding: softcover
Volume 349 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-262-4
ISBN online: 978-1-64368-263-1

Learning and Reasoning in Hybrid Structured Spaces

Artificial intelligence often has to deal with uncertain scenarios, such as a partially observed environment or noisy observations. Traditional probabilistic models, while being very principled approaches in these contexts, are incapable of dealing with both algebraic and logical constraints. Existing hybrid continuous/discrete models are typically limited in expressivity, or do not offer any guarantee on the approximation errors.

This book, Learning and Reasoning in Hybrid Structured Spaces, discusses a recent and general formalism called Weighted Model Integration (WMI), which enables probabilistic modeling and inference in hybrid structured domains. WMI-based inference algorithms differ with respect to most alternatives in that probabilities are computed inside a structured support involving both logical and algebraic relationships between variables. While the research in this area is at an early stage, we are witnessing an increasing interest in the study and development of scalable inference procedures and effective learning algorithms in this setting.

This book details some of the most impactful contributions in context of WMI-based inference in the last 5 years. Moreover, by providing a gentle introduction to the main concepts related to WMI, the book can be useful for both theoretical researchers and practitioners alike.

Author: Morettin, P.
Pages: 110
Binding: softcover
Volume 350 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-266-2
ISBN online: 978-1-64368-267-9

Abstraction in Ontology-based Data Management

Effectively documenting data services is a crucial issue in any organization, not only for governing data but also for interoperation purposes. Indeed, in order to fully realize the promises and benefits of a data-driven society, data-driven approaches need to be resilient, transparent, and fully accountable.

This book, Abstraction in Ontology-based Data Management, proposes a new approach to automatically associating formal semantic description to data services, thus bringing them into compliance with the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles. The approach is founded on the Ontology-based Data Management (OBDM) paradigm, in which a domain ontology is used to provide a high-level semantic layer mapped to the source schema of an organization containing data, thus abstracting from the technical details of the data layer implementation. A formal framework for a novel reasoning task in OBDM, called Abstraction, is introduced in which a data service is assumed to be expressed as a query over the source schema, and the aim is to derive a query over the ontology that semantically describes the given data service best with respect to the underlying OBDM specification. In a general scenario that uses the most popular languages in the OBDM literature, an in-depth complexity analysis of two computational problems associated with the framework is carried out. Also investigated is the problem of expressing abstractions in a non-monotonic query language as well as the impact of adding inequalities. Regarding the latter, the problem of answering queries with inequalities over lightweight ontologies is first studied. Lastly, the author illustrates how the achieved results contribute to new results in the Semantic Web context and in the Relational Database theory.

The book will be of interest to all those engaged in Artificial Intelligence and Data Management.

Author: Cima, G.
Pages: 268
Binding: softcover
Volume 348 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-258-7
ISBN online: 978-1-64368-259-4

Design Studies and Intelligence Engineering

The technologies applied in design studies vary from basic theories to more application-based systems, and intelligence engineering technologies – such as computer-aided industrial design, human factor design, and greenhouse design – play a significant role in design science. Intelligence engineering technologies encompass both theoretical and application perspectives, such as computational technologies, sensing technologies, and video detection. Intelligence engineering is multidisciplinary in nature, promoting cooperation, exchange and discussion between organizations and researchers from diverse fields.

This book presents the proceedings of DSIE2021, the 2021 International Symposium on Design Studies and Intelligence Engineering, held in Hangzhou, China, on 27 & 28 November 2021. This annual conference invites renowned experts from around the world to speak on their specialist topics, providing a platform for many professionals and researchers from industry and academia to exchange and discuss recent advances in the field of design studies and intelligence engineering. The 210 submissions received were rigorously reviewed, and each of the 50 papers presented here was selected based on scores from three or four referees. Papers cover a very wide range of topics, from the design of a pneumatic soft finger with two joints, and the emotion of texture, to the design evaluation of a health management terminal for the elderly, and a multi-robot planning algorithm with quad tree map division for obstacles of irregular shape.

Providing a varied overview of recent developments in design and intelligence engineering, this book will be of interest to researchers and all those working in the field.

Editors: Jain, L.C., Balas, V.E., Wu, Q., Shi, F.
Pages: 480
Binding: softcover
Volume 347 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-256-3
ISBN online: 978-1-64368-257-0

Information Modelling and Knowledge Bases XXXIII

The technology of information modelling and knowledge bases addresses the complexities of modelling in digital transformation and digital innovation, reaching beyond the traditional borders of information systems and academic research in computer science.

This book presents 21 papers from the 31st International conference on Information Modeling and Knowledge Bases (EJC 2021), hosted by the Department Informatik of the University of Applied Sciences in Hamburg, Germany, and held as a virtual event from 7 to 9 September 2021 due to restrictions caused by the Corona virus. The conference provides a research forum for academics and practitioners dealing with information and knowledge to exchange scientific results and experiences, and EJC 2021 covered a wide range of themes extending knowledge discovery through conceptual modeling, knowledge and information modeling and discovery, linguistic modeling, cross-cultural communication and social computing, environmental modeling and engineering, and multimedia data modeling and systems. As always, the conference was open to new topics related to its main themes, meaning the content emphasis of the EJC conferences is always able to adapt to the changes taking place in the research field, and the 21 papers included here after rigorous review, selection and upgrading are the result of presentations, comments, and discussions during the conference.

Providing an up to the minute overview of the technology of information modeling and knowledge bases, the book will be of interest to all those working in the field.

Editors: Tropmann-Frick, M., Jaakkola, H., Thalheim, B., Kiyoki, Y., Yoshida, N.
Pages: 346
Binding: softcover
Volume 343 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-242-6
ISBN online: 978-1-64368-243-3

Formal Ontology in Information Systems

Proceedings of the Twelfth International Conference (FOIS 2021)

Formal Ontology in Information Systems (FOIS) is the flagship conference of the International Association for Ontology and its Applications, a non-profit organization promoting interdisciplinary research and international collaboration at the intersection of philosophical ontology, linguistics, logic, cognitive science, and computer science.

This book presents the 11 papers accepted for the 12th edition of FOIS. The conference was held from 13-17 September 2021 in Bozen-Bolzano, Italy, as a hybrid event with some participants attending on-site in Bolzano and others attending virtually online. The papers are divided into 3 sections and cover a wide range of topics: (1) Foundations, addressing fundamental issues; (2) Applications and Methods, presenting novel uses, systems, tools, and approaches; and (3) Domain Ontology, describing well-formed ontologies in particular subject areas.

Editors: Neuhaus, F., Brodaric, B.
Pages: 190
Binding: softcover
Volume 344 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-248-8
ISBN online: 978-1-64368-249-5

Proceedings of CECNet 2021

The 11th International Conference on Electronics, Communications and Networks (CECNet), November 18-21, 2021

It is almost impossible to imagine life today without the electronics, communications and networks we have all come to take for granted. The 6G network is currently under development and some chips able to operate at the Terahertz (THz) scale have already been introduced, so the next decade will probably see the consolidation of 6G-based technology, as well as many compliant devices.

This book presents the proceedings of the 11th International Conference on Electronics, Communications and Networks (CECNet 2021), initially planned to be held from 18-21 November 2021 in Beijing, China, but ultimately held as an online event due to ongoing COVID-19 restrictions. The CECNet series is now an established annual event attracting participants in the interrelated fields of electronics, computers, communications and wireless communications engineering and technology from around the world. Careful review by program committee members, who took into consideration the breadth and depth of those research topics that fall within the scope of CECNet, resulted in the selection of the 88 papers presented here from the 325 submissions received. This represents an acceptance rate of around 27%.

Providing an overview of current research and developments in these rapidly evolving fields, the book will be of interest to all those working with digital communications networks.

Editors: Tallón-Ballesteros, A.J.
Pages: 786
Binding: softcover
Volume 345 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-240-2
ISBN online: 978-1-64368-241-9

Neuro-Symbolic Artificial Intelligence: The State of the Art

Neuro-symbolic artficial intelligence is an emerging subfield of artificial intelligence (AI) that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together.

This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses and, from a cognitive-science perspective, there is a question as to how a neural system can perform symbol manipulation, and how the representational differences between these two approaches can be bridged. The book presents 17 overview papers, all by authors who have made significant contributions in the past few years and starting with a historic overview first seen in 2016. With just seven months elapsed from invitation to authors to final copy, the book is as up-to-date as a published overview of this subject can be.

Based on the editors’ own desire to understand the current state of the art, this book reflects the breadth and depth of the latest developments in neuro-symbolic AI, and will be of interest to students, researchers, and all those working in the field of artificial intelligence.

Editors: Hitzler, P., Sarker, M.K.
Pages: 408
Binding: softcover
Volume 342 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-244-0
ISBN online: 978-1-64368-245-7

Legal Knowledge and Information Systems

JURIX 2021: The Thirty-fourth Annual Conference, Vilnius, Lithuania, 8-10 December 2021

Traditionally concerned with computational models of legal reasoning and the analysis of legal data, the field of legal knowledge and information systems has seen increasing interest in the application of data analytics and machine learning tools to legal tasks in recent years.

This book presents the proceedings of the 34th annual JURIX conference, which, due to pandemic restrictions, was hosted online in a virtual format from 8 – 10 December 2021 in Vilnius, Lithuania. Since its inception as a mainly Dutch event, the JURIX conference has become truly international and now, as a platform for the exchange of knowledge between theoretical research and applications, attracts academics, legal practitioners, software companies, governmental agencies and judiciary from around the world. A total of 65 submissions were received for this edition, and after rigorous review, 30 of these were selected for publication as long papers or short papers, representing an overall acceptance rate of 46 %. The papers are divided into 6 sections: Visualization and Legal Informatics; Knowledge Representation and Data Analytics; Logical and Conceptual Representations; Predictive Models; Explainable Artificial Intelligence; and Legal Ethics, and cover a wide range of topics, from computational models of legal argumentation, case-based reasoning, legal ontologies, smart contracts, privacy management and evidential reasoning, through information extraction from different types of text in legal documents, to ethical dilemmas.

Providing an overview of recent advances and the cross-fertilization between law and computing technologies, this book will be of interest to all those working at the interface between technology and law.

Editors: Schweighofer, E.
Pages: 272
Binding: softcover
Volume 346 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-252-5
ISBN online: 978-1-64368-253-2

Modern Management based on Big Data II and Machine Learning and Intelligent Systems III

Proceedings of MMBD 2021 and MLIS 2021

It is data that guides the path of applications, and Big Data technologies are enabling new paths which can deal with information in a reasonable time to arrive at an approximate solution, rather than a more exact result in an unacceptably long time. This can be particularly important when dealing with an urgent issue such as that of the COVID-19 pandemic.

This book presents the proceedings of two conferences: MMBD 2021 and MLIS 2021.

The MMBD conference deals with two main subjects; those of Big Data and Modern Management. The MLIS conference aims to provide a platform for knowledge exchange of the most recent scientific and technological advances in the field of machine learning and intelligent systems. Both conferences were originally scheduled to be held from 8-11 November 2021, in Quanzhou, China and Xiamen, China respectively. Both conferences were ultimately held fully online on the same dates, hosted by Huaqiao University in Quanzhou and Xiamen respectively.

The book is in two parts, and contains a total of 78 papers (54 from MMBD2021 and 24 from MLIS2021) selected after rigorous review from a total of some 300 submissions. The reviewers bore in mind the breadth and depth of the research topics that fall within the scope of MMBD and MLIS, and selected the 78 most promising and FAIA mainstream-relevant contributions for inclusion in this two-part volume. All the papers present original ideas or results of general significance supported by clear reasoning, compelling evidence and rigorous methods.

Editors: Tallón-Ballesteros, A.J.
Pages: 736
Binding: softcover
Volume 341 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-224-2
ISBN online: 978-1-64368-225-9


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