Style is a word that people think they understand. Most people recognize artifacts like buildings and clothes as being exemplars of particular styles, and they know words like Rococo and Art Deco as names for styles. They can recognize stylistic similarities not only in one sort of artifact but also across wide ranges of different things, such as buildings, furniture, artworks, clothes, music, and even manners. However, “style” is a slippery notion: the word has been used in a variety of senses since the ancient Greeks first thought about the differences in how people wrote or painted, and it is still used to refer to different things.

Analyses of styles in design have paid little attention to how people see style and how designers use perceptions of style to guide designing. Although formal and computational methods for analyzing styles and generating designs provide impressively parsimonious accounts of what some styles are, they do not address many of the factors that influence how humans understand styles. The subtlety of human style judgments raises challenges for computational approaches to style. This paper differentiates between a range of distinct meanings of “style” and explores how designers and ordinary people learn and apply perceptual similarity classes and style concepts in different situations to interpret and create designed artifacts. A range of psychological evidence indicates that style perception is dependent on knowledge and involves the interaction of perceptual recognition of style features and explanatory inference processes that create a coherent understanding of an object as an exemplar of a style. This article concludes by outlining how formal style analyses can be used in combination with psychological research to develop a fuller understanding of style perception and creative design.

At its root level, style is actually an esthetic agreement between people. The question is, how can esthetic agreements be modeled and measured in artificial intelligence? This paper offers a formal theory called EVE′ and applies it to a novel test bed of dynamic drawings that combine features of music and sketching. The theory provides mathematical measures of expectations, violations, and explanations, which are argued to be the atomic components of the esthetic experience. The approach employs Bayesian methods to extend information measures proposed in other research. In particular, it is shown that information theory is useful at an entropic level to measure expectations (E) of signals and violations (V) of expectations, but that Bayesian theory is needed at a semantic level to measure explanations (E′) of meaning for the signals. The entropic and semantic measures are then combined in further measures of tension and pleasure at an esthetic level that is actually style.

Generative specifications have been used to systematically codify established styles in several design fields including architecture and product design. We examine how designers explore new designs in the early stages of product development as they manipulate and interpret shape representations. A model of exploration is proposed with four types of shape descriptions (contour, decomposition, structure, and design) and the results of the exploration are presented. Generative rules are used to provide consistent stylistic changes first within a given decomposition and second through changing the structure. Style expresses both the analytical order of explanation and the synthetic complexity of exploration. The model of exploration is consistent with observations of design practice. The application of generative design methods demonstrates a logical pattern for early stage design exploration. The model provides the basis for tools to assist designers in exploring families of designs in a style and for following new interpretations that move the exploration from one family to another.

In the push for new vehicle designs, the distinctions between vehicle classes are quickly becoming blurred. We use shape grammars to quantify the differences between vehicle classes through the application of class-specific rules and the constraint of rule applications to within parametric ranges determined for each class. This allows for the development of new vehicle forms that clearly fall within a class or purposefully cross over the boundaries of classes and mix rules and ranges to create unique and interesting cross-over vehicles.

Style is an ordering principle by which to structure artifacts in a design domain. The application of a visual order entails some explicit grouping property that is both cognitively plausible and contextually dependent. Central to cognitive–contextual notions are the type of representation used in analysis and the flexibility to allow semantic interpretation. We present a model of visual style based on the concept of similarity as a qualitative context-dependent categorization. The two core components of the model are semantic feature extraction and self-organizing maps (SOMs). The model proposes a method of categorizing two-dimensional unannotated design diagrams using both low-level geometric and high-level semantic features that are automatically derived from the pictorial content of the design. The operation of the initial model, called Q-SOM, is then extended to include relevance feedback (Q-SOM:RF). The extended model can be seen as a series of sequential processing stages, in which qualitative encoding and feature extraction are followed by iterative recategorization. Categorization is achieved using an unsupervised SOM, and contextual dependencies are integrated via cluster relevance determined by the observer's feedback. The following stages are presented: initial per feature detection and extraction, selection of feature sets corresponding to different spatial ontologies, unsupervised categorization of design diagrams based on appropriate feature subsets, and integration of design context via relevance feedback. From our experiments we compare different outcomes from consecutive stages of the model. The results show that the model provides a cognitively plausible and context-dependent method for characterizing visual style in design.

This paper claims that style, in addition to being identified by common visible physical characteristics of form, can be thought of in terms of a set of common abstract characteristics. A prototype computational design support tool is described that explores this idea in the domain of architecture. The Architect's Collaborator (TAC) supports articulation and evaluation of abstract characteristics of style (e.g., experiential characteristics such as privacy and shelter) and does so by mapping abstract characteristics to details of physical form. The implementation of TAC is described and successful experiments are reported in which abstract characteristics of Frank Lloyd Wright's Prairie houses were mapped to physical form characteristics and used to evaluate Prairie and non-Prairie houses.