The 'what' and 'how' of learning in design, invited paper

Previous experiences hold a wealth of knowledge which we often take for granted and use unknowingly through our every day working lives. In design, those experiences can play a crucial role in the success or failure of a design project, having a great deal of influence on the quality, cost and development time of a product. But how can we empower computer based design systems to acquire this knowledge? How would we use such systems to support design? This paper outlines some of the work which has been carried out in applying and developing Machine Learning techniques to support the design activity; particularly in utilising previous designs and learning the design process

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development

Mutual knowledge evolution in team design

This paper presents an investigation into the phenomenon of mutual knowledge evolution in team working using protocol data. The focus is on whether mutual knowledge evolution in agents exists, and if so, what triggers this phenomenon. Section 2 presents the nature of team design. Team design is a collective problem solving and knowledge co-constructed process (Bonner, 1959; Nguifo et al, 1999). When members in a design team work together, they can therefore produce a result that individuals may not readily produce, which is called team synergy (Prasad, 1995). Section 3 presents the hypothesis that designers can mutually evolve their design idea and learn from each other. An example of mutual knowledge evolution process is posited. In section 4, the analysis of mutual knowledge evolution using protocol data is carried out. Through the analysis, the phenomenon of mutual knowledge evolution has been observed and the reasons that trigger the phenomenon have been discussed. The conclusion is made in section 5 and future research has been identified. Collective learning in team design has been presented by Wu and Duffy (Wu and Duffy, 2002). In this paper the focus is specifically on investigating mutual knowledge evolution, i.e., a design phenomenon in which the agents mutually evolve their design knowledge and co-construct the design solution

Using situation theory to model information flow in design

Information flow is intensive during a design process, where delivering timely and appropriate information is required. Sonnenwald [2] identified 13 communication roles that emerged during four multidisciplinary design situations in the USA and Europe. She statedthat participants from different disciplines, organisations and cultures come to the design situation with pre-existing patterns of working activities, and specialised work languages. Different methods to represent information flow activities are used, varying in different companies, different disciplines, and different teams, which may cause misunderstandings particularly among design teams composed of different organisations. In this sense, it is important to present information flow in a rigorous way. Eastman and Shirley [3] developed amodel of design information flow. The model dealt with design information management, reflecting entities, constraints, design states, design document accessed modes, transactions, and version identifiers. But, the development of their model was not based upon a theoretical foundation. In this paper, we develop an alternative model to present information flow indesign based on a foundation of situation theory. The model may serve to analysis design information system and provide a basis for investigating the situatedness of designinformation flow. To be able to represent information flow we should firstly study its phenomena. Based on Sim's formalism of design activities, the theory of Speech Acts, Computer Supported Cooperative Work (CSCW, and other works studying information flow, an example model for information flow in design is developed. A discussion of the strengths and weaknesses of this representation method is carried out

Design reuse research : a computational perspective

This paper gives an overview of some computer based systems that focus on supporting engineering design reuse. Design reuse is considered here to reflect the utilisation of any knowledge gained from a design activity and not just past designs of artefacts. A design reuse process model, containing three main processes and six knowledge components, is used as a basis to identify the main areas of contribution from the systems. From this it can be concluded that while reuse libraries and design by reuse has received most attention, design for reuse, domain exploration and five of the other knowledge components lack research effort

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

A novel model of learning in design

Learning in design is a phenomenon that has been observed in design practice by many researchers. The observation that designers learn is supported by protocol studies in design that experienced designers can reach satisfactory design solutions more effectively than novice/naive designers. That there was no comprehensive model or theory of learning in design to explain the phenomenon was identified by Sim. Hence a need was raised to develop a comprehensive model of learning in design that can describe the phenomenon and therefore serve as a basis to develop effective and efficient design support system(s)

Knowledge transformers : a link between learning and creativity

The purpose of this paper is to investigate whether knowledge transformers that are featured in the learning process are also present in the creative process. First, this was achieved by reviewing accounts of inventions and discoveries with the view of explaining them in terms of knowledge transformers. Second, this was achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers

Modularity in support of design for re-use

We explore the structuring principle of modularity with the objective of analysing its current ability to meet the requirements of a 're-use' centred approach to design. We aim to highlight the correlation's between modular design and 're-use', and argue that it has the potential to aid the little-supported process of 'design-for-re-use'. In fulfilment of this objective we not only identify the requirements of 'design-for-re-use', but also propose how modular design principles can be extended to support 'design-for-re-use'

A formalism for coupled design learning activities

This paper presents a formalism to represent the inextricable link that exists between design and learning. It provides an approach to study and analyse the complex relationships that may exist between design and learning. It suggests that design and learning are linked at the knowledge level (epistemic link), in a temporal manner and in a purposeful manner through the design and learning goal