A comparison of case-based reasoning and regression analysis approaches for cost uncertainty modeling

This thesis presents case-based reasoning approach for estimating the cost and modeling cost uncertainty of a new product in the concept selection stage. Case-based reasoning (CBR) is an approach which uses old cases/experiences to understand and solve new problems. The CBR approach consists of creating a knowledge-base (or database) containing past cases (products), defining a new case (concept), retrieving cases similar to the new case, and adjusting the solution of the retrieved cases to the new case. The first paper compares case-based reasoning, in studying the effects of varying design attribute specifications on cost estimation accuracy and cost distribution reliability. Case-based reasoning with cost estimation is compared with three methods: analogy-based cost estimation, case-based reasoning without cost adjustment, and regression analysis. Four automobile concepts with similar performance attribute specifications but varying design attribute specifications are defined and the comparison is made using leave-one-out cross-validation technique to a knowledge-base of 345 automobiles. The second paper further establishes case-based reasoning with cost adjustment by studying the optimum number of design attributes for specifying a concept. The results show that case-based reasoning and with cost adjustment performed best for cost estimation accuracy and cost distribution reliability when one design attribute is specified for the concept in addition to performance attributes --Abstract, page iv

References to past designs

Designing by adaptation is almost invariably a dominantambiguity feature of designing, and references to past designs are ubiquitous in design discourse. Object references serve as indices into designers' stocks of design concepts, in which memories for concrete embodiments and exemplars are tightly bound to solution principles. Thinking and talking by reference to past designs serves as a way to reduce the overwhelming complexity of complex design tasks by enabling designers to use parsimonious mental representations to which details can be added as needed. However object references can be ambiguous, and import more of the past design than is intended or may be desirable

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

Geoscience after IT: Part J. Human requirements that shape the evolving geoscience information system

The geoscience record is constrained by the limitations of human thought and of the technology for handling information. IT can lead us away from the tyranny of older technology, but to find the right path, we need to understand our own limitations. Language, images, data and mathematical models, are tools for expressing and recording our ideas. Backed by intuition, they enable us to think in various modes, to build knowledge from information and create models as artificial views of a real world. Markup languages may accommodate more flexible and better connected records, and the object-oriented approach may help to match IT more closely to our thought processes

Learning strategies in interpreting text: From comprehension to illustration

Learning strategies can be described as behaviours and thoughts a learner engages in during learning that are aimed at gaining knowledge. Learners are, to use Mayer’s (1996) constructivist definition, ‘sense makers’. We can therefore position this to mean that, if learners are sense makers, then learning strategies are essentially cognitive processes used when learners are striving to make sense out of newly presented material. This paper intends to demonstrate that such thoughts and behaviours can be made explicit and that students can co-ordinate the basic cognitive processes of selecting, organising and integrating. I will discuss two learning strategies which were developed during three cycles of an action research enquiry with a group of illustration students. While each cycle had its own particular structure and aims, the main task, that of illustrating a passage of expository text into an illustration was a constant factor. The first learning strategy involved assisting students develop ‘macropropositions’—personal understandings of the gist or essence of a text (Louwerse and Graesser, 2006; Armbruster, Anderson and Ostertag, 1987; Van Dijk & Kintsch, 1983). The second learning strategy used a form of induction categorised as analogical reasoning (Holyoak, 2005; Sloman and Lagnado, 2005). Both strategies were combined to illustrate the expository text extract. The data suggests that design students benefit from a structured approach to learning, where thinking processes and approaches can be identified and accessible for other learning situations. The research methodology is based on semi-structured interviews, questionnaires, developmental design (including student notes) and final design output. All student names used are pseudonyms. The text extract from ‘Through the Magic Door’ an essay Sir Arthur Conan Doyle, (1907) has been included as it provides context to analysis outcomes, student comments and design outputs. Keywords: Action Research; Illustration; Macrostructures; Analogical Reasoning; Learning Strategies</p

Form, science, and narrative in the anthropocene

A significant strand of contemporary fiction engages with scientific models that highlight a constitutive interdependency between humanity and material realities such as the climate or the geological history of our planet. This article looks at the ways in which narrative may capture this human-nonhuman interrelation, which occupies the foreground of debates on the so-called Anthropocene. I argue that the formal dimension of scientific knowledge-as manifested by diagrams or metaphors used by scientists-is central to this narrative remediation. I explore two analogical strategies through which narrative may pursue a formal dialogue with science: clusters of metaphorical language and the global structuring of the plot. Rivka Galchen's novel Atmospheric Disturbances (2008), for instance, builds on a visual representation of meteorological patterns in a storm (lifted from an actual scientific paper) to stage the narrator's mental illness. Two other contemporary works (Orfeo by Richard Powers and A Tale for the Time Being by Ruth Ozeki) integrate scientific models through the overall design of the plot. By offering close readings of these novels, I seek to expand work in the area of New Formalism and show how formal choices are crucial to bringing together the human-scale world and more-than-human phenomena

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

The Reliability of Memory: An Argument from the Armchair

The “problem of memory” in epistemology is concerned with whether and how we could have knowledge, or at least justification, for trusting our apparent memories. I defend an inductive solution—more precisely, an abductive solution—to the problem. A natural worry is that any such solution would be circular, for it would have to depend on memory. I argue that belief in the reliability of memory can be justified from the armchair, without relying on memory. The justification is, roughly, that my having the sort of experience that my apparent memory should lead me to expect is best explained by the hypothesis that my memories are reliable. My solution is inspired by Harrod’s (1942) inductive solution. Coburn (1960) argued that Harrod’s solution contains a fatal flaw. I show that my solution is not vulnerable to Coburn’s objection, and respond to a number of other, recent and likely objections

Memory, Imagery, and Self-Knowledge

One distinct interest in self-knowledge concerns whether one can know about one’s own mental states and processes, how much, and by what methods. One broad distinction is between accounts that centrally claim that we look inward for self-knowledge (introspective methods) and those that claim that we look outward for self-knowledge (transparency methods). It is here argued that neither method is sufficient, and that we see this as soon as we move beyond questions about knowledge of one’s beliefs, focusing instead on how one distinguishes, for oneself, one’s veridical visual memories from mere (non-veridical) visual images. Given robust psychological and phenomenal similarities between episodic memories and mere imagery, the following is a genuine question that one might pose to oneself: “Do I actually remember that happening, or am I just imagining it?” After critical analysis of the transparency method (advocated by Byrne 2010, following Evans 1982) to this latter epistemological question, a brief sketch is offered of a more holistic and inferential method for acquisition of broader self-knowledge (broadly following the interpretive-sensory access account of Carruthers 2011). In a slogan, knowing more of the mind requires using more of the mind

Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning