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

authors Reffat, Rabee M.
year 2000
title Computational Situated Learning in Designing - Application to Architectural Shape Semantics
source The University of Sydney, Faculty of Architecture
summary Learning the situatedness (applicability conditions), of design knowledge recognised from design compositions is the central tenet of the research presented in this thesis. This thesis develops and implements a computational system of situated learning and investigates its utility in designing. Situated learning is based on the concept that "knowledge is contextually situated and is fundamentally influenced by its situation". In this sense learning is tuned to the situations within which "what you do when you do matters". Designing cannot be predicted and the results of designing are not based on actions independent of what is being designed or independent of when, where and how it was designed. Designers' actions are situation dependent (situated), such that designers work actively with the design environment within the specific conditions of the situation where neither the goal state nor the solution space is completely predetermined. In designing, design solutions are fluid and emergent entities generated by dynamic and situated activities instead of fixed design plans. Since it is not possible in advance to know what knowledge to use in relation to any situation we need to learn knowledge in relation to its situation, i.e. learn the applicability conditions of knowledge. This leads towards the notion of the situation as having the potential role of guiding the use of knowledge.

Situated Learning in Designing (SLiDe) is developed and implemented within the domain of architectural shape composition (in the form of floor plans), to construct the situatedness of shape semantics. An architectural shape semantic is a set of characteristics with a semantic meaning based on a particular view of a shape such as reflection symmetry, adjacency, rotation and linearity. Each shape semantic has preconditions without which it cannot be recognised. Such preconditions indicate nothing about the situation within which this shape semantic was recognised. The situatedness or the applicability conditions of a shape semantic is viewed as, the interdependent relationships between this shape semantic as the design knowledge in focus, and other shape semantics across the observations of a design composition. While designing, various shape semantics and relationships among them emerge in different representations of a design composition. Multiple representations of a design composition by re-interpretation have been proposed to serve as a platform for SLiDe. Multiple representations provide the opportunity for different shape semantics and relationships among them to be found from a single design composition. This is important if these relationships are to be used later because it is not known in advance which of the possible relationships could be constructed are likely to be useful. Hence, multiple representations provide a platform for different situations to be encountered. A symbolic representation of shape and shape semantics is used in which the infinite maximal lines form the representative primitives of the shape.

SLiDe is concerned with learning the applicability conditions (situatedness), of shape semantics locating them in relation to situations within which they were recognised (situation dependent), and updating the situatedness of shape semantics in response to new observations of the design composition. SLiDe consists of three primary modules: Generator, Recogniser and Incremental Situator. The Generator is used by the designer to develop a set of multiple representations of a design composition. This set of representations forms the initial design environment of SLiDe. The Recogniser detects shape semantics in each representation and produces a set of observations, each of which is comprised of a group of shape semantics recognised at each corresponding representation. The Incremental Situator module consists of two sub-modules, Situator and Restructuring Situator, and utilises an unsupervised incremental clustering mechanism not affected by concept drift. The Situator module locates recognised shape semantics in relation to their situations by finding regularities of relationships among them across observations of a design composition and clustering them into situational categories organised in a hierarchical tree structure. Such relationships change over time due to the changes taken place in the design environment whenever further representations are developed using the Generator module and new observations are constructed by the Recogniser module. The Restructuring Situator module updates previously learned situational categories and restructures the hierarchical tree accordingly in response to new observations.

Learning the situatedness shape semantics may play a crucial role in designing if designers pursue further some of these shape semantics. This thesis illustrates an approach in which SLiDe can be utilised in designing to explore the shapes in a design composition in various ways; bring designers! attention to potentially hidden features and shape semantics of their designs; and maintain the integrity of the design composition by using the situatedness of shape semantics. The thesis concludes by outlining future directions for this research to learn and update the situatedness of design knowledge within the context of use; considering the role of functional knowledge while learning the situatedness of design knowledge; and developing an autonomous situated agent-based designing system.

series thesis:PhD
email rabee@arch.usyd.edu.au
full text file.pdf (3,161,523 bytes)
references Cumincad : CUMINCAD References : TOC for Robots

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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sigradi2014_201g7, 90ae, df64, 0902, bc68, f6c6, 5de8, 84f1, 4f20, 840c, 820b, b3f1, 4bde, sigradi2016_364pp, 89bc, b5da, 35f7, 3fe5, 9ab3, ecaade2016_152v41, 60aa, 9914, 3885, ecaade2014_147x33, ascaad2016_022e9, 3112, ba79, ecaade2014_089u21, c35b, 39d4, ecaade2015_180w38, c87c, 5afb, d319, 0a8b, 7c03, 1014, ecaade2017_118hh, 2cad, 428f, b7b7, 9090, caadria2016_033t2, c534, 876f, 4d1d, 54f6, ecaade2015_103p20, 081e, 2ddc, 5b92, 530c, 2fa3, <