The discipline of design studies applies various technologies, from basic theory to application systems, while intelligence engineering encompasses computer-aided industrial design, human-factor design, and greenhouse design, and plays a major part within design science. Intelligence engineering technologies also include topics from theory and application, such as computational technologies, sensing technologies, and video detection. This book presents the proceedings of DSIE2023, the 2023 International Symposium on Design Studies and Intelligence Engineering, held on 28 & 29 October 2023 in Hangzhou, China. The conference provides a platform for professionals and researchers from industry and academia to present and discuss recent advances in the fields of design studies and intelligence engineering. It also fosters cooperation among the organizations and researchers involved in these overlapping fields, and invites internationally renowned professors to further explore these topics in some depth, providing the opportunity for them to discuss the technical presentations with conference participants. In all, 275 submissions were received for the conference, 105 of which were accepted after thorough review by 3 or 4 referees for presentation at the conference and inclusion here. Providing a valuable overview of the latest developments, the book will be of interest to all those working in the fields of 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.

Over the last decade research into design processes utilizing ideas and models drawn from artificial intelligence has resulted in a better understanding of design -- particularly routine design -- as a process. Indeed, most of the current research activity directly or indirectly deals only with routine design. Not surprisingly, many practicing designers state that the level of understanding represented by these models is only of mild interest because they fail to embody any ideas about creativity. This volume provides a set of chapters in the areas of modeling creativity and knowledge-based creative design that examines the potential role and form of computer-aided design which supports creativity. It aims to define the state-of-the-art of computational creativity in design as well as to identify research directions. Published at a time when the field of computational creativity in design is still immature, it should influence the directions of growth and assist the field in reaching maturity.

This book, Advanced Tools and Methods for Treewidth-Based Problem Solving, contains selected results from the author’s PhD studies, which were carried out from 2015 to 2021. For his PhD thesis, Markus Hecher received the EurAI Dissertation Award 2021 and the GI Dissertation Award 2021, amongst others. The aim of the book is to present a new toolkit for using the structural parameter of treewidth to solve problems in knowledge representation and reasoning (KR) and artificial intelligence (AI), thereby establishing both theoretical upper and lower bounds, as well as methods to deal with treewidth efficiently in practice. The key foundations outlined in the book provide runtime lower bounds – under reasonable assumptions in computational complexity – for evaluating quantified Boolean formulas and logic programs which match the known upper bounds already published in 2004 and 2009. The general nature of the developed tools and techniques means that a wide applicability beyond the selected problems and formalisms tackled in the book is anticipated, and it is hoped that the book will serve as a starting point for future theoretical and practical investigations, which will no doubt establish further results and gain deeper insights.

Deep learning has been used with great success in a number of diverse applications, ranging from image processing to game playing, and the fast progress of this learning paradigm has even been seen as paving the way towards general artificial intelligence. However, the current deep learning models are still principally limited in many ways. This book, ‘Deep Learning with Relational Logic Representations’, addresses the limited expressiveness of the common tensor-based learning representation used in standard deep learning, by generalizing it to relational representations based in mathematical logic. This is the natural formalism for the relational data omnipresent in the interlinked structures of the Internet and relational databases, as well as for the background knowledge often present in the form of relational rules and constraints. These are impossible to properly exploit with standard neural networks, but the book introduces a new declarative deep relational learning framework called Lifted Relational Neural Networks, which generalizes the standard deep learning models into the relational setting by means of a ‘lifting’ paradigm, known from Statistical Relational Learning. The author explains how this approach allows for effective end-to-end deep learning with relational data and knowledge, introduces several enhancements and optimizations to the framework, and demonstrates its expressiveness with various novel deep relational learning concepts, including efficient generalizations of popular contemporary models, such as Graph Neural Networks. Demonstrating the framework across various learning scenarios and benchmarks, including computational efficiency, the book will be of interest to all those interested in the theory and practice of advancing representations of modern deep learning architectures.

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.

Showcasing exemplars of how various aspects of design research were successfully transitioned into and influenced, design practice, this book features chapters written by eminent international researchers and practitioners from industry on the Impact of Design Research on Industrial Practice. Chapters written by internationally acclaimed researchers of design analyse the findings (guidelines, methods and tools), technologies/products and educational approaches that have been transferred as tools, technologies and people to transform industrial practice of engineering design, whilst the chapters that are written by industrial practitioners describe their experience of how various tools, technologies and training impacted design practice. The main benefit of this book, for educators, researchers and practitioners in (engineering) design, will be access to a comprehensive coverage of case studies of successful transfer of outcomes of design research into practice; as well as guidelines and platforms for successful transfer of research into practice.

This book presents the state of the art of artificial intelligence techniques applied to structural engineering. The 28 revised full papers by leading scientists were solicited for presentation at a meeting held in Ascona, Switzerland, in July 1998. The recent advances in information technology, in particular decreasing hardware cost, Internet communication, faster computation, increased bandwidth, etc., allow for the application of new AI techniques to structural engineering. The papers presented deal with new aspects of information technology support for the design, analysis, monitoring, control and diagnosis of various structural engineering systems.

The Routledge Companion to Design Research offers a comprehensive examination of design research, celebrating the plurality of design research and the wide range of conceptual, methodological, technological and theoretical approaches evident in contemporary design research. This volume comprises 39 original and high quality design research chapters from contributors around the world, with offerings from the vast array of disciplines in and around modern design praxis, including areas such as industrial and product design, visual communication, interaction design, fashion design, service design, engineering and architecture. The Companion is divided into five distinct sections with chapters that examine the nature and process of design research, the purpose of design research, and how one might embark on design research. They also explore how leading design researchers conduct their design research through formulating and asking questions in novel ways, and the creative methods and tools they use to collect and analyse data. The Companion also includes a number of case studies that illustrate how one might best communicate and disseminate design research through contributions that offer techniques for writing and publicising research. The Routledge Companion to Design Research will have wide appeal to researchers and educators in design and design-related disciplines such as engineering, business, marketing, computing, and will make an invaluable contribution to state-of-the-art design research at postgraduate, doctoral, and post-doctoral levels and teaching across a wide range of different disciplines.