A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

Computational Models of Innovative and Creative Design Processes

Constraint programming offers facilities for problem modelling, constraint propagation and search. This paper discusses the resulting benefits for practical applications which exploit these facilities. The modelling facilities are particularly exploited in applications to verification, both of circuits and of real time control systems. The propagation facilities are exploited in applications involving user feedback and graphical interfaces. The search facilities are exploited in applications such as scheduling and resource allocation, which involve combinatorial problems. The paper surveys applications under each of these three headings.

Practical applications of constraint programming

Every design problem begins with an effort to achieve fitness between two entities: the form in question and its context. The form is the solution to the problem; the context defines the problem. We want to put the context and the form into effortless contact or frictionless coexistence, i.e., we want to find a good fit. For a good fit to occur in practice, one vital condition must be satisfied. It must have time to happen. In slow-changing, traditional, unselfconscious cultures, a form is adjusted soon after each slight misfit occurs. If there was good fit at some stage in the past, no matter how removed, it will have persisted, because there is an active stability at work. Tradition and taboo dampen and control the rate of change in an unselfconscious culture's designs. It is important to understand that the individual person in an unselfconscious culture needs no creative strength. He does not need to be able to improve the form, only to make some sort of change when he notices a failure. The changes may not always be for the better; but it is not necessary that they should be, since the operation of the process allows only the improvements to persist. Unselfconscious design is a process of slow adaptation and error reduction. In the unselfconscious process there is no possibility of misconstruing the situation. Nobody makes a picture of the context, so the picture cannot be wrong. But the modern, selfconscious designer works entirely from a picture in his mind - a conceptualization of the forces at work and their interrelationships - and this picture is almost always wrong. To achieve in a few hours at the drawing board what once took centuries of adaptation and development, to invent a form suddenly which clearly fits its context - the extent of invention necessary is beyond the individual designer. A designer who sets out to achieve an adaptive good fit in a single leap is not unlike the child who shakes his glass-topped puzzle fretfully, expecting at one shake to arrange the bits inside correctly. The designer's attempt is hardly as random as the child's is; but the difficulties are the same. His chances of success are small because the number of factors which must fall simultaneously into place is so enormous. The process of design, even when it has become selfconscious, remains a process of error-reduction. No complex system will succeed in adapting in a reasonable amount of time or effort unless the adaptation can proceed component by component, each component relatively independent of the others. The search for the right components, and the right way to build the form up from these components, is the greatest challenge faced by the modern, selfconscious designer. The culmination of the modern designer's task is to make every unit of design both a component and a system. As a component it will fit into the hierarchy of larger components that are above it; as a system it will specify the hierarchy of smaller components of which it itself is made.

Notes on the Synthesis of Form. By Christopher Alexander. Pp. 191. 54s. 1964. (Harvard University Press.)

Accelerating preliminary eco-innovation design for products that integrates case-based reasoning and TRIZ method

Reference LIA-ARTICLE-1993-002View record in Web of Science Record created on 2006-12-13, modified on 2017-05-12

/pdf/exploring-case-based-building-design-cadre-4rx9gqdmqb.pdf

Exploring Case-Based Building Design-CADRE

Any design process involves two kinds of knowledge: domain knowledge and design knowledge. In this paper, we focus on the formulation of design knowledge within the building domain. Representations which formulate building design knowledge include production rules, shape grammars, prototypes and cases. To avoid blind search, design knowledge must be indexed by function according to different aspects. We argue that search for a solution that accommodates several aspects is best carried out through iterative refinement of cases, and that a precise geometrical model of the case is required to link different aspects. This leads us to employ cases as a design knowledge representation and adaptation as a reasoning methodology for design. We describe a procedure for adaptation of building structures to new environments using interleaved processes of dimensional and topological modifications. We show results of a prototype which implements this procedure.

Adaptation of Spatial Design Cases

A building design task can be viewed according to many different abstractions. For example, an architect views a building as a collection of spaces with particular properties, while a civil engineer might consider it to be a structure made up of load-bearing elements. In order to produce a workable design, it is important to be able to combine these different viewpoints. Difficulties associated with combining viewpoints lead to what we term the integration problem. The casebased design (CBD) methodology presented in this paper provides an opportunity to model the intentions of several professions and trades using cases of previous good designs. Therefore, CBD provides pre-formulated solutions to integration problems. We describe a prototype design system, Cadre, which applies CBD to several examples of building design.

Integrated Case-Based Building Design

Case-based design reasoning provides support for design of buildings and products. This paper describes a computer based system that uses case-based reasoning for design of spatial aspects of buildings. We describe the processes of case adaptation where an existing design case is dimensionally and topologically adapted to suit a new design problem, and case combination where parts of more than one design case are combined to develop a new design solution.

Case-Based Spatial Design Reasoning

Constraint satisfaction with numerical constraints is a fundamental problem in automated design. Traditional constraint satisfaction procedures are designed for problems where there is one constant set of constraints. In design, it is often necessary to solve a dynamic constraint satisfaction problem (DCSP) where the set of applicable constraints depends on design choices. Finding a solution in DCSP requires searching among different consistent sets of constraints to find one which has an acceptable numerical solution. In this paper, an algorithm is described which supports the propagation of numerical intervals in such a dynamic environment.

Dynamic constraint satisfaction in a bridge design system

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