This paper is an exploration of how different computer techniques can be brought to bear in the analysis of catalogues of design precedents. The techniques include automatic classification, various kinds of frequency analysis, clustering, and connectionist techniques. Connectionist techniques are demonstrated as forming the basis of a useful design tool that facilitates a rudimentary kind of design dialogue. This dialogue involves a kind of exploration within the constraints of an atomistic representation schema in which key features are determined a priori.
Get full access to this article
View all access options for this article.
References
1.
AlexanderC, 1964Notes on the Synthesis of Form (Harvard University Press, Cambridge, MA).
2.
BallardD HBrownC M, 1982Computer Vision (Prentice-Hall, Englewood Cliffs, NJ).
CoyneR D, 1990a, “Modelling the emergence of design descriptions across schemata”Environment and Planning B: Planning and Design18437–458.
5.
CoyneR D, 1990b, “Design reasoning without explanations”AI Magazine11(4) 72–80.
6.
CoyneR DNewtonS, 1990, “Design reasoning by association”Environment and Planning B: Planning and Design1739–56.
7.
CoyneR DNewtonSSudweeksF, 1989, “Modelling the emergence of schemas in design reasoning”, in Proceedings of Modeling Creativity and Knowledge-based Creative Design Eds GeroJ SMaherM L, (University of Sydney, Sydney) pp 173–205.
EverittB, 1974Cluster Analysis (John Wiley, New York).
10.
FisherD H, 1987, “Knowledge acquisition via incremental conceptual clustering”Machine Learning2139–172.
11.
GadamerH-G, 1975Truth and Method (Sheed and Ward, London).
12.
GoodmanD, 1987The Complete HyperCard Handbook (Bantam Books, New York).
13.
HopfieldJ J, 1982, “Neural networks and physical systems with emergent collective computational abilities”Proceedings of the National Academy of Sciences, USA792554–2558.
14.
HuntE BMartinJStoneP J, 1966Experiments in Induction (Academic Press, New York).
Merleau-PontyM, 1962Phenomenology of Perception (Routledge and Kegan Paul, London).
17.
MichalskiR S, 1980, “Knowledge acquisition through conceptual clustering: A theoretical framework and algorithm for partitioning data into conjunctive concepts”International Journal of Policy Analysis and Information Systems4219–243.
18.
MichalskiR S, 1983, “A theory and methodology of inductive learning”Artificial Intelligence20111–161.
19.
MinskyM, 1975, “A framework for representing knowledge”, in The Psychology of Computer Vision Ed. WinstonP H, (McGraw-Hill, New York) pp 211–277.
20.
QuinlanJ R, 1979, “Discovering rules by induction from large collections of examples”, in Expert Systems in the Micro-electronic Age Ed. MichieD, (Edinburgh University Press, Edinburgh) pp 168–201.
21.
RosenblattF, 1962Principles of Neurodynamics (Spartan Press, East Lansing, MI).
22.
RumelhartD EMcClellandJ L, (Eds), 1987Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1, Foundations (MIT Press, Cambridge, MA).
23.
RumelhartD ESmolenskyPMcClellandJ LHintonG E, 1987, “Schemata and sequential thought processes in PDP models”, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1, Foundations Eds RumelhartD EMcClellandJ L, (MIT Press, Cambridge, MA) pp 7–57.
24.
SchankR C, 1982Dynamic Memory: A Theory of Reminding and Learning in Computers and People (Cambridge University Press, Cambridge).
