CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id a01a
authors Morgan, Fred and Pohlmann, Richard W. (eds.)
year 1993
title Education and Practice: The Critical Interface [Conference Proceedings]
source ACADIA Conference Proceedings / ISBN 1-880250-02-0 / Texas (Texas / USA) 1993, 185 p.
summary For many years architects and educators have debated questions about appropriate approaches to architectural education: HOW should education serve the profession? What should be our educational objectives? What level of computer expertise and potential should society expect of our graduates? At times the pragmatic concerns of practitioners have clashed with the theoretical concerns of educators. Most would agree that both points of view have merit; it is in establishing an appropriate balance that we most disagree. Now, the discussions have expanded to include issues of computer-aided design. Software and hardware vendors find themselves in the middle of a difficult but interesting dilemma. While supporting systems optimized for professional practice they are asked to supply radically different systems for educational use.
series ACADIA
last changed 1999/02/25 09:06

_id cb67
authors Paranandi, Murali
year 1995
title Roof Modeling Using Architectural Semantics Paradigm
source Computing in Design - Enabling, Capturing and Sharing Ideas [ACADIA Conference Proceedings / ISBN 1-880250-04-7] University of Washington (Seattle, Washington / USA) October 19-22, 1995, pp. 333-350
summary This paper presents an approach to developing the computer aided architectural design systems investigating architectural semantics paradigm and void modeling representation as a method. A prototypical system called FRED(Facile Roof Editor & Designer) was developed incorporating structural logic and characteristics of roof in its basic representation and its operational behavior constrained by distinct attributes of a roof. Design of Hip, Pitch, Multi-level, and Flat roofs in Solid and Shell forms was made possible by extracting from an existing building or creating them as independent entities. The implementation successfully demonstrates that incorporating architectural semantics in the basic representation of a CAD system allows architects to create and test roof morphology fairly quickly, accurately, and fluidity for ideation.
keywords Solid, Shell, Void Modeling, Architectural Semantics, Roofs, Pitch, Hip, Eaves, Ideation
series ACADIA
last changed 2003/05/16 17:23

_id 1920
authors Riesbeck, C. and Schank, R.C.
year 1989
title Inside Case-based Reasoning
source Lawrence Erlbaum Associates, Hillsdale, NJ
summary Case-based reasoning, broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents is using case-based reasoning. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving. Case-based reasoning (CBR) has been formalized as a four-step process:N 1. Retrieve: Given a target problem, retrieve cases from memory that are relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. 2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue -- an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. 4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his newfound procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule-induction algorithmsP of machine learning.N Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. For instance, when Fred mapped his procedure for plain pancakes to blueberry pancakes, he decided to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization. For instance, if a rule-induction algorithm were given recipes for plain pancakes, Dutch apple pancakes, and banana pancakes as its training examples, it would have to derive, at training time, a set of general rules for making all types of pancakes. It would not be until testing time that it would be given, say, the task of cooking blueberry pancakes. The difficulty for the rule-induction algorithm is in anticipating the different directions in which it should attempt to generalize its training examples. This is in contrast to CBR, which delays (implicit) generalization of its cases until testing time -- a strategy of lazy generalization. In the pancake example, CBR has already been given the target problem of cooking blueberry pancakes; thus it can generalize its cases exactly as needed to cover this situation. CBR therefore tends to be a good approach for rich, complex domains in which there are myriad ways to generalize a case.
series other
last changed 2003/04/23 13:14

_id sigradi2015_3.221
id sigradi2015_3.221
authors Schunemann, Frederick Gorsten; Celani, Gabriela
year 2015
title Integration between analog and digital in Architecture
source SIGRADI 2015 [Proceedings of the 19th Conference of the Iberoamerican Society of Digital Graphics - vol. 1 - ISBN: 978-85-8039-135-0] Florianópolis, SC, Brasil 23-27 November 2015, pp. 126-128.
summary Contemporary architecture relies heavily on high-technology, high-precision methods, such as parametric design and digital fabrication. However, often times architects need to work with preexisting shapes and structures, which are not only irregular but also ephemeral. This paper describes a design exercise in which a bamboo structure was digitized and had a roof system parametrically modeled and fabricated for it.
series SIGRADI
last changed 2016/03/10 08:59

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