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

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

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)

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

Intelligent computational sketching support for conceptual design

Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury.It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer

Shape matching and clustering

Generalising knowledge and matching patterns is a basic human trait in re-using past experiences. We often cluster (group) knowledge of similar attributes as a process of learning and or aid to manage the complexity and re-use of experiential knowledge [1, 2]. In conceptual design, an ill-defined shape may be recognised as more than one type. Resulting in shapes possibly being classified differently when different criteria are applied. This paper outlines the work being carried out to develop a new technique for shape clustering. It highlights the current methods for analysing shapes found in computer aided sketching systems, before a method is proposed that addresses shape clustering and pattern matching. Clustering for vague geometric models and multiple viewpoint support are explored

Operational design co-ordination : an agent based approach

Operational design co-ordination has been identified as the basis for an approach to engineering design management that is more comprehensive than those that currently exist. As such, an integrated and holistic approach to operational design co-ordination has been developed that enables design to be managed in a coherent, appropriate and timely manner. Furthermore, the approach has been implemented within an agent-based software system, called the Design Co-ordination System, which has been applied to an industrial case study involving the computational design analysis of turbine blades. This application demonstrates that managing and adjusting in real-time in an operationally co-ordinated manner enables reductions in the time taken to complete the turbine blade design process to be achieved

A triangulation approach for design research

Triangulation has been adopted in social science in the study of the same phenomenon through applying and combining several data sources, research methods, investigators, and theoretical schemes. From a post-positivism view point, this paper presents a triangulation approach in design research from two perspectives, data sources and research methods. Data triangulation was achieved through collecting data from multiple sources including company design documents, student design projects, and company design projects. Different research methods, e.g. interview, content analysis, protocol analysis, and questionnaire, were used to conduct data collection and analysis into a particular aspect of design, the nature of coupling design artefact and process knowledge. It was found that triangulation can provide an effective means for design research

An integrated decision support environment for organisational decision making

Traditional decision support systems are based on the paradigm of a single decision maker working at a stand-alone computer or terminal who has a specific decision to make with a specific goal in mind. Organisational decision support systems aim to support decision makers at all levels of an organisation (from executive, middle management managers to operators), who have a variety of decisions to make, with different priorities, often in a distributed environment. Such systems are designed and developed with extra functionality to meet the challenge. This paper proposes an Integrated Decision Support Environment (IDSE) for organisational decision making. The IDSE is designed and developed based on distributed client/server networking, with a combination of tight and loose integration approaches for information exchange and communication. The prototype of the IDSE demonstrates a good balance between flexibility and reliability

Knowledge re-use for decision support

Effective decision support has already been identified as a fundamental requirement for the realisation of Network Enabled Capability. Decision making itself is a knowledge-intensive process, and it is known that right decisions can only be reached based on decision maker's good judgement, which in turn is based on sufficient knowledge. It is not unusual for decision makers to make incorrect decisions because of insufficient knowledge. However, it is not always possible for decision makers to have all the knowledge needed for making decisions in complex situations without external support. The re-use of knowledge has been identified as providing an important contribution to such support, and this paper considers one, hitherto unexplored, aspect of how this may be achieved. This paper is concerned with the computational view of knowledge re-use to establish an understanding of a knowledge-based system for decision support. The paper explores knowledge re-use for decision support from two perspectives: knowledge provider's and knowledge re-user's. Key issues and challenges of knowledge re-use are identified from both perspectives. A structural model for knowledge re-use is proposed with initial evaluation through empirical study of both experienced and novice decision maker's behaviour in reusing knowledge to make decisions. The proposed structural model for knowledge re-use captures five main elements (knowledge re-uers, knowledge types, knowledge sources, environment, and integration strategies) as well as the relationships between the elements, which forms a foundation for constructing a knowledge-based decision support system. The paper suggests that further research should be investigating the relationship between knowledge re-use and learning to achieve intelligent decision support