What Does Shape Grammar Implementation Tell Us About Artificial Intelligence in Architectural Design?

Purpose One of the major barriers to implementing artificial intelligence (AI) in fashion design is possibly higher consumer reluctance to accept AI-designed (vs. human-designed) products. How can brands alleviate the negative responses to AI-designed products? To answer this question, this research tests the role of product innovativeness in determining the levels of consumer resistance to AI designs. Design/methodology/approach The hypotheses were developed based on the literature on algorithm aversion and appreciation, fashion design evaluation, and mind perception theory. To test the hypotheses, we conducted three online experiments using Amazon Mturk through CloudResearch platform. Findings While a general preference for human designs over AI designs was found, the negative attitudes toward AI designs were stronger for low-innovative products but weaker for high-innovative products. This is because, according to the conditional process analysis, participants perceived AI-designed products as less original compared to human-designed products when innovativeness level was low. However, this pattern was not shown when innovativeness level was high. Practical implications The findings show the potential for overcoming aversion to AI-designed fashion products. Brands utilizing AI in design are recommended to aim for highly innovative designs, characterized by deconstruction fashion and avant-garde approaches and emphasize innovativeness values when promoting AI-designed products. Originality/value This research sheds light on how and why consumers' negative responses to AI designs vary depending on the final product design, contributing to the discourse on fashion creativity in the era of generative AI from consumers' perspectives.

Artificial intelligence dimensions and design features for the knowledge-based work of the future: a socio-technical perspective

The ability to work with ambiguity and compute new designs based on both defined and emergent shapes are unique advantages of shape grammars. Realizing these benefits in design practice requires the implementation of general purpose shape grammar interpreters that support: (a) the detection of arbitrary subshapes in arbitrary shapes and (b) the application of shape rules that use these subshapes to create new shapes. The complexity of currently available interpreters results from their combination of shape computation (for subshape detection and the application of rules) with computational geometry (for the geometric operations need to generate new shapes). This paper proposes a shape grammar implementation method for three-dimensional circular arcs represented as rational quadratic Bézier curves based on lattice theory that reduces this complexity by separating steps in a shape computation process from the geometrical operations associated with specific grammars and shapes. The method is demonstrated through application to two well-known shape grammars: Stiny's triangles grammar and Jowers and Earl's trefoil grammar. A prototype computer implementation of an interpreter kernel has been built and its application to both grammars is presented. The use of Bézier curves in three dimensions opens the possibility to extend shape grammar implementations to cover the wider range of applications that are needed before practical implementations for use in real life product design and development processes become feasible.

In new product development, quickly generating many product form concepts that a potential consumer prefers is a challenge. This paper presents the inaugural multiagent shape grammar implementation (MASGI) to automatically generate product form designs according to a preference function that can represent designer or consumer design preference. Additionally, the multiagent system creates a flexible shape grammar implementation that enables modifications to the shape grammar as the form design space changes. The method is composed of three subprocesses: a shape grammar interpreter that implements the shape grammar, an agent system that chooses which shape grammar rules to implement and the parametric design choices according to a preference function, and a preference investigator that determines the preference function, which constraints the automated form design process.

Artificial intelligence (AI) design traditionally prioritizes full automation and broadest data coverage yet often overlooks the contextual realities of pre-existing biases in datasets and human agency in real-world applications. This article explores how cross-disciplinary collaboration can transform this paradigm by opening AI's ‘black box' of full automation to public accountability, human operation and real-world use. Based on anthropologists’ direct collaboration with computer scientists in a machine learning (ML) AI design project, this project proposes cross-disciplinary methodological innovations for algorithm design and takes up the fairness issue as an exemplary domain to experiment with our new ML model framework. We developed a new ML framework that moves beyond conventional accuracy-coverage trade-offs to incorporate a human-operable three-way balance between accuracy, fairness, and coverage. In this framework, we foreground the real-world contexts and human agency at multiple stages of AI design, from data training, decision-making, interface design to model testing. The results demonstrate both the promise and challenges of bridging academic disciplines and connecting lab-based AI development with real-world needs. While our collaboration has made AI design more accountable, it also highlights enduring tensions in balancing competing priorities like fairness, accuracy, and coverage.

The Purpose of the Study: This paper addresses how artificial intelligence (AI) plays a role in the world of design and how it affects the concept of originality. The paper examines the use of AI in areas such as graphic design, logo design, painting, original designs and web design, and discusses the innovations that this technology brings to design processes. The paper also considers the positive and negative effects of AI on designers. Positive effects include the acceleration of design processes and the access to a wider creative spectrum. On the other hand, the impact of AI on originality is a controversial issue. It is questioned how original the designs produced with AI are and whether these designs have artistic value. Literature Review/Background: The impact of artificial intelligence (AI) technology in the field of design and the reconsideration of the concept of originality are mentioned. While AI offers speed and efficiency in design processes, creative solutions have been addressed through learning from data and algorithms. While the innovations and efficiency advantages offered by AI expand the creative capacities of designers, the concepts of originality and personal expression are evaluated. Methodology: The research design of the study was qualitative, document scanning method was used as the data collection method, and content analysis method was used to analyse the data. Using the document scanning method, the effects of artificial intelligence in design and the concepts of originality were discussed. Findings: The impact of artificial intelligence (AI) technology in the field of design necessitates a reconsideration of the concept of originality. While AI offers speed and efficiency in design processes, it produces creative solutions through learning from data and algorithms. However, originality in this process can often be derived from existing data or based on style transfers. While the innovations and efficiency advantages offered by AI expand the creative capacities of designers, it may cause you to question the concepts of originality and personal expression. Conclusion: the paper suggests that AI is an important tool in the design world and predicts that this technology will become even more widespread in the future. However, it is emphasised that AI should be used carefully and consciously in creative processes.

Research into shape grammar implementation has been largely concerned with rectilinear shapes and there has been limited research into implementation on shapes composed of curves. This reflects developments of the shape grammar formalism which has been defined largely according to straight lines, planes, and associated volumes. In this paper, implementation of shape grammars on curved shapes is examined using algorithms for shape operations on shapes composed of parametric curves. These algorithms have been implemented in a shape grammar interpreter for shapes composed of quadratic Bézier curves, which is illustrated via application of a shape grammar that generates Celtic knotwork patterns. Implementing shape grammars on shapes composed of Bézier curves highlights difficulties that arise when the shape grammar formalism is applied to curved shapes, and the paper concludes with a discussion that explores these difficulties and indicates potential implications for the shape grammar formalism.

This paper presents a general 3D shape grammar interpreter named GRAMATICA and illustrates its use for the implementation of several shape grammars, including the one used in the design module of a specifi c shape grammar for mass-customized housing, called DESIGNA. The underlying shape representation, generation and control are discussed. The resulting shape grammar interpreter tries to support designers’ ways of thinking and working by acting as a bridge between shape grammars, the formalism that captures a design process, and a CAD application, for post-processing the computed design. This bridge is implemented by Rosetta, which ensures portability among different CAD applications.

Shapes, structures and shape grammar implementation

Chapter 13 - Application of machine learning and artificial intelligence in design, optimization, and control of power electronics converters for renewable energy-based technologies

Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses

Computer-based shape grammar implementations aim to support creative design exploration by automating rule-application. This paper reviews existing shape grammar implementations in terms of their algorithmic complexity, extends the definition of shape grammars with sets of transformations for rule application, categorizes (parametric and non-parametric) sets of transformations, and analyses these categories in terms of the resulting algorithmic complexity. Specifically, it describes how different sets of transformations admit different numbers of targets (i.e., potential inputs) for rule application. In the non-parametric case, this number is quadratic or cubic, while in the parametric case, it can be non-polynomial, depending on the size of the target shape. The analysis thus yields lower bounds for the algorithmic complexity of shape grammar implementations that hold independently of the employed algorithm or data structure. Based on these bounds, we propose novel matching algorithms for non-parametric and parametric shape grammar implementation and analyze their complexity. The results provide guidance for future, general-purpose shape grammar implementations for design exploration.

A growing body of literature has focused on understanding and addressing workplace artificial intelligence (AI) design failures. However, past work has largely overlooked the role of the devaluation of worker expertise in shaping the dynamics of AI development and deployment. In this paper, we examine the case of feminized labor: a class of devalued occupations historically misnomered as “women’s work,” such as social work, K-12 teaching, and home healthcare. Drawing on literature on AI deployments in feminized labor contexts, we conceptualize AI Failure Loops : a set of interwoven, sociotechnical failure modes that help explain how the systemic devaluation of workers’ expertise negatively impacts, and is impacted by, AI design, evaluation, and governance practices. These failures demonstrate how misjudgments on the automatability of workers’ skills can lead to AI deployments that fail to bring value to workers and, instead, further diminish the visibility of workers’ expertise. We discuss research and design implications for workplace AI, especially for devalued occupations.

The concept of shape grammars has often been proposed to improve or support creative design processes. Shape grammar implementations have the potential to both automate parts of the design process and allow exploration of design alternatives. In many of the existing implementations, the main focus is either on capturing the rationale of a particular existing grammar or on allowing designers to develop a new grammar. However, little attention is typically given to the actual representation of the design space that can be explored in the interface of the implementation. With such representation, a shape grammar implementation could properly support designers who are still in the process of designing and may not yet have a clear shape grammar in mind. In this article, an approach and a proof-of-concept software system is proposed for a shape grammar implementation that provides a visual and interactive way to support design space exploration in a creative design process. We describe the method by which this software system can be used and focus on how designers can interact with the exploration process. In particular, we point out how the proposed approach realizes several important amplification strategies to support design space exploration.

E-marketing refers to the use of technology and artificial intelligence to analyze and interpret marketing data. In addition to providing recommendations and guidance to improve marketing campaigns, improve user experience, and increase sales. These tools include data analytics, machine learning, data classification and aggregation, voice and image recognition, and natural language. Therefore, this study aimed to identify the effect of artificial intelligence (AI) in Design on E-Marketing in Companies. This study used a survey design with a quantitative approach, with the target group being workers of E-Marketing companies. A 198 surveys were also gathered from Jordanian E-Marketing companies, however, 187 questionnaires were judged viable for study. Where the results indicate AI positively affects E-Marketing, and natural language processing, analytical models, content marketing, and digital marketing affect E-Marketing.