Computational Situated Learningin Designing
Application to Architectural Shape Semantics
Rabee M. Reffat
B.Arch (Hons), M.Sc. Arch. Eng.
A Thesis Submitted for the Degree ofDoctor of Philosophy
Department of Architectural and Design ScienceFaculty of ArchitectureUniversity of Sydney
Rabee M. Reffat 2000
This thesis is dedicated to the loving memory of my beloved father.My Lord bestow your mercy on him as he cherished me in my childhood.
I would like to express my sincere appreciation and gratitude to the people in the KeyCentre of Design Computing and Cognition, KCDCC, for their encouragement andsupport. First, I would like to thank my supervisor Professor John S. Gero for hiscontinuous support in my Ph.D. program. Professor Gero introduced me to a hybridworld of design computing, artificial intelligence and cognitive science. He taught mehow to express ideas, approach a research problem and the need to be persistent toaccomplish my goals. His patient support during the last four years helped me to bringthe research presented in this thesis to a successful conclusion.
A special thanks goes to Professor Mary Lou Maher and Dr. Mike Rosenman for theirvaluable comments on the thesis proposal and to Professors Nigel Cross and TimSmithers for discussing my research with me during their visit to KCDCC. I would liketo thank the faculty members and staff at KCDCC especially Dr. Scott Chase, Dr.Simeon Simoff, Paul Murty, Fay Sudweeks, Dr.Vladmir Kazakov, Dr. Masaki Suwa, Dr.Manolya Kavakli, Joe Nappa, Doug Scoular, Andrew Winter, Anne Christian and mycolleagues Dr. Jose Damski, Dr. Josiah Poon, Dr. Thorsten Schnier, Dr. AnnaCicognani, Dr. Lan Ding, Dr. Soohoon Park, Dr. Andres Gomez, Philip Tomlinson, KatyBridge, Gourabmoy Nath, Gerard Gabriel, Guang Shi, Jaroslaw Kulinski, RobertSaunders, Fei Li, Hsien-Hui Tang, Chin Chin Kau, Eonyong Kim, Ellina Yukhina, JustinClayden, Greg Smith and Stephen Clarke. I am grateful for the financial support of boththe Key Centre of Design Computing and Cognition and the University of Sydneyscholarships. My enormous appreciation belongs to Dr. Edward L. Harkness forproofreading and reviewing the English in this thesis.
The heart of my dedication belongs to my beloved family: my mother, brothers andsisters, my wife and my son for the depth of their love. I am indebted to my wife whodevoted her life to our small family lovingly and willingly. The presence of my family andmy circle of friends, new and old, near and far, provided me with passion, support andidentity.
Learning the situatedness (applicability conditions), of design knowledge recognisedfrom design compositions is the central tenet of the research presented in this thesis. Thisthesis develops and implements a computational system of situated learning andinvestigates its utility in designing. Situated learning is based on the concept that"knowledge is contextually situated and is fundamentally influenced by its situation". Inthis sense learning is tuned to the situations within which "what you do when you domatters". Designing cannot be predicted and the results of designing are not based onactions independent of what is being designed or independent of when, where and how itwas designed. Designers' actions are situation dependent (situated), such that designerswork actively with the design environment within the specific conditions of the situationwhere neither the goal state nor the solution space is completely predetermined. Indesigning, design solutions are fluid and emergent entities generated by dynamic andsituated activities instead of fixed design plans. Since it is not possible in advance toknow what knowledge to use in relation to any situation we need to learn knowledge inrelation to its situation, ie learn the applicability conditions of knowledge. This leadstowards the notion of the situation as having the potential role of guiding the use ofknowledge.
Situated Learning in Designing (SLiDe) is developed and implemented within thedomain of architectural shape composition (in the form of floor plans), to construct thesituatedness of shape semantics. An architectural shape semantic is a set ofcharacteristics with a semantic meaning based on a particular view of a shape such asreflection symmetry, adjacency, rotation and linearity. Each shape semantic haspreconditions without which it cannot be recognised. Such preconditions indicatenothing about the situation within which this shape semantic was recognised. Thesituatedness or the applicability conditions of a shape semantic is viewed as, theinterdependent relationships between this shape semantic as the design knowledge infocus, and other shape semantics across the observations of a design composition. Whiledesigning, various shape semantics and relationships among them emerge in differentrepresentations of a design composition. Multiple representations of a designcomposition by re-interpretation have been proposed to serve as a platform for SLiDe.Multiple representations provide the opportunity for different shape semantics andrelationships among them to be found from a single design composition. This isimportant if these relationships are to be used later because it is not known in advancewhich of the possible relationships could be constructed are likely to be useful. Hence,multiple representations provide a platform for different situations to be encountered. Asymbolic representation of shape and shape semantics is used in which the infinitemaximal lines form the representative primitives of the shape.
SLiDe is concerned with learning the applicability conditions (situatedness), of shapesemantics 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 designerto develop a set of multiple representations of a design composition. This set ofrepresentations forms the initial design environment of SLiDe. The Recogniser detectsshape semantics in each representation and produces a set of observations, each of whichis comprised of a group of shape semantics recognised at each correspondingrepresentation. The Incremental Situator module consists of two sub-modules, Situatorand Restructuring Situator, and utilises an unsupervised incremental clusteringmechanism not affected by concept drift. The Situator module locates recognised shapesemantics in relation to their situations by finding regularities of relationships amongthem across observations of a design composition and clustering them into situationalcategories organised in a hierarchical tree structure. Such relationships change over timedue to the changes taken place in the design environment whenever furtherrepresentations are developed using the Generator module and new observations areconstructed by the Recogniser module. The Restructuring Situator module updatespreviously learned situational categories and restructures the hierarchical treeaccordingly in response to new observations.
Learning the situatedness shape semantics may play a crucial role in designing ifdesigners pursue further some of these shape semantics. This thesis illustrates anapproach in which SLiDe can be utilised in designing to explore the shapes in a designcomposition in various ways; bring designers' attention to potentially hidden features andshape semantics of their designs; and maintain the integrity of the design composition byusing the situatedness of shape semantics. The thesis concludes by outlining futuredirections for this research to learn and update the situatedness of design knowledgewithin the context of use; considering the role of functional knowledge while learning thesituatedness of design knowledge; and developing an autonomous situated agent-baseddesigning system.
Acknowledgments iiiSummary ivContents viList of Figures ixList of Tables xiii
Chapter 1 Introduction 11.1 Motivation 21.2 Aims and Objectives 51.3 Scope and Limitations 81.4 Organisation of the Thesis 8
Chapter 2 Background 92.1 Designing and Situatedness 9
2.1.1 Designing: rationality vs. reflection-in-action 92.1.2 Designing actions: planned vs. situated 102.1.3 The situated view of cognition/action 112.1.4 Designing as a situated activity 12
126.96.36.199 Situatedness in designing 188.8.131.52 Situated versus procedural and declarative knowledge 14
2.2 What is the "situation" and how it is constructed? 152.3 Situated Learning 17
Context-sensitive learning as related to situated learning 182.4 Machine Learning in Designing 19
Learning systems in designing 202.5 Situated Learning in Designing 24
2.5.1 Incremental learning systems in designing 262.5.2 Accommodating the situatedness within computational systems in
Chapter 3 Multiple Representations: A Platform for SituatedLearning in Designing 29
3.1 Multiple Representations while Designing 303.2 Multiple Representations of an Architectural Shape 34
3.2.1 Initial representation of a shape 343.2.2 Development of multiple representations 37
184.108.40.206 Unbounded n-sided subshapes representations 3220.127.116.11 Bounded n-sided subshapes representations 418.104.22.168 Emergent shapes 422.214.171.124 Figure and ground 44
3.3 The Role of Multiple Representations in Situated Learning in Designing 46
Chapter 4 Situated Learning of Architectural Shape Semantics 494.1 Shape Semantics in Architectural Drawings 49
4.1.1 Selection of shape semantics 504.1.2 Recognising various shape se