Exploring Architectural Design Cases1 Marcus Herzog*, Riccardo Peratello*, Christian Kühn**, Wolfgang Slany* *Information Systems Department (E184-2), Technical University of Vienna, Paniglg. 16, A-1040 Vienna, Austria {herzog, peratel, wsi}@vexpert.dbai.tuwien.ac.at **Building Design Theory Department (E252), Technical University of Vienna, Karlsplatz 13, A-1040 Vienna, Austria
[email protected]
Abstract Architects use old solutions as source for inspiration in the design process. We present a model for the representation of architectural design cases. We face the problem of formalizing ill-structured domain knowledge without a consistent theoretical base. We further investigate the relationship between architectural design information and the languages used to represent that information. The theoretical model of language-game abstractions (LGAs) is presented as a means to study these relationships. Based on that model we propose an information structure that supports intelligent reasoning. This is used to retrieve appropriate case material about some design goal. The definition of similarity between design cases is a crucial point in the design of the retrieval algorithm. We use AI-techniques, which are also interesting for other Hypertext applications. 1 Introduction Precedents and analogy play an important role in design processes. Many design artefacts have to satisfy the same constraints. Old solutions can be used to extract design rules or can even be treated as solutions to subgoals of a problem. This is especially true for architecture, but also for other technical domains or even for fields like diagnosis problem solving. Architects often use previous design solutions to investigate the context of a new design task. They search for the most appropriate effects that can be attained in a unique context [Arch87]. Old solutions may fit the new context through adaptation, thus being a major source of new ideas. Using precedents in the design task poses two problems: (a) the storing and managing of large case bases; (b) the retrieval of appropriate case materials. Storing deals with the problem of decomposing large cases. A model of case representation has to fulfil the knowledge demands required to support reasoning over the cases. This is a basic requirement for an intelligent retrieval process. Surveying the literature is a common non computer-based strategy to solve the case retrieval problem. To support the reader, books on architectural design are most often organized employing some sort of typology [Vidl77]. Although architectural typology tends to solve the question, what kind of object is a work of architecture, there is no sound theory on which to base the organizational structure of architectural
information. Instead one has to cope with the problem of different theories and typological approaches competing with each other of time. Analogies in architecture depend more on the perception devoted to some theoretical approach than on physical properties of an architectural case. In the following we want to present our approach to modelling the representation of architectural design cases. In [Kühn93] we introduced language game abstractions (LGAs) as means for representing architectural design knowledge. Section 2 gives an overview of this model. Section 3 emphasizes briefly the information structure of our current approach. In section 4, retrieval issues are approached and examples of exploration strategies are given. Section 5 summarizes the results and gives further research perspectives. 2 Language Game Abstractions Newell and Simon [Newe63] describe the design process as a search problem. Well defined goal states can be resolved starting from an initial state and using transitions between states. In this model, design is the path from the initial state to the a priori defined goal state. Smithers and Troxell [Smit90] view the problem of design as an exploration process. Exploration emphasizes more the structure of the problem space, defined by properties and operations on these properties. Design is the transformation of an initial incomplete set of requirements into a final set of properties, which define the solution. Within this model of design as exploration, we want to describe the structure and content of design cases. Design cases represent the space of possible solutions. In architecture, natural language, diagrams, drawings, plans, etc. are used to represent design cases. This bulk of information cannot be formalized as a whole. Instead we need to extract a suitable indexing vocabulary. Terms have to be defined to serve as descriptors. On the contrary to text-retrieval systems these terms cannot be extracted directly from the documents. We have to design a descriptive language. This will be complicated because concepts of designers about design problems vary. Following Ludwig Wittgenstein's language-game theory, we use words to describe objects, but also, these objects indicate the meaning of the word [Witt52]. We cannot separate the language used to describe entities from the described entities. See also [Kühn91] for a more detailed analysis regarding the role of natural language in architectural design knowledge.
1This work has been partly supported by the "Hochschuljubiläumsfonds der Gemeinde Wien", grant number 163-91.
A LGA is the combination of design cases and indexing vocabulary used to describe these cases. The structure of the resulting representation is mainly influenced by the relations between cases and terms as well as by relations within the set of cases and the set of terms. A LGA is used to investigate these relations simultaneously. We cannot define one LGA that covers all possible relations in advance. Instead a bunch of different LGAs will appear, competing with each other. Their validity cannot be proven in a mathematical sense. We can only distinguish between effective and non-effective LGAs. 3 Information Structure Taking LGAs as a theoretical model we are now presenting an information structure derived from it. The basic structure is a network with nodes representing "data" and links representing "semantic relations". We introduce two levels: one for pure information, the other for explicit knowledge on that information (see Fig. 1). In the information level we use hypermedia techniques to render the architectural design cases. Architectural cases are decomposed into information chunks that try to give an impression of the building as close to reality as possible. Various media types like text, picture, video, sound, etc. are supported. The model of hypertext as a way to represent information that can be browsed is augmented by the definition of different types of nodes and links. The knowledge level consists of a semantic network [Quil68]. The crucial point in the design of the semantic network is the definition of a taxonomy of links. These links are used to compute some measurement of similarity between the information nodes. Figure 1 summarizes the basic elements of our information structure: (a) Information nodes [I] are information chunks characterized by their structure and appearance [Megh91]. (b) Descriptor nodes [D] represent the vocabulary of a LGA. (c) Links between information nodes and descriptor nodes [i-d] define the abstraction level. Descriptors are used as index for the information nodes. (d) Links between information nodes [i-i] render relations between information chunks, which cannot be mapped onto relations between descriptor nodes. These links are not used in the inference algorithm and can be seen as "classical" hypertext links. (e) Links between descriptor nodes [d-d] express different relations between the concepts used to refer to the information chunks. Such relations can be, e.g. generalization, classification, aggregation, etc. 4 Exploration Issues Techniques of Information Retrieval (IR) evolved to support searching in large, unstructured text-documents using some sort of query. Recent works show that these techniques also can be
applied for searching complex structured hypermedia documents. [Frie88]. Different models of IR exist in the research field [Salt83]. The major problem of IR in Hypertext is the uncertainty resulting from the use of natural language. There are different approaches to adopt conventional IR methods for the use in Hypertext. Croft and Turtle [Crof89] base their model on "belief networks" using Bayesian inference. Lucarella [Luca89] uses plausible inference founded on fuzzy set theory. Also these seem to be possible solutions no real-world problems have been solved so far. The basic function of these IR methods can be perceived as filtering. Nodes in the Hypertext network are ranked according to their "similarity" to a virtual node defined by a query statement. Most retrieval inferences work by spreading activation, starting from one concept that fits best the query. If not enough nodes are retrieved, more related concepts are activated, thus getting more related documents. For example, if an architect is asked to look for theaters s(he) also might look for concert halls, because both types of buildings are used for public performances. If the boundaries of the domain are getting weaker, s(he) also will explore a football stadium, a town hall, and a cinema. As outlined in the introduction we are especially interested in the retrieval of prior design solutions in the respect to some design goal. To achieve a good retrieval result
we have to investigate the relationship between the indexing vocabulary and the document. 4.1 Exploration using search algorithms (1) Exploration within a LGA. Descriptors are used to abstract design cases. Relations between descriptors abstract relations between design cases, e.g. the concept sacred building is a generalization of both church and synagogue. We can use these relations to infer related concepts. In Fig. 1, the descriptors D5 and D6 are related to the descriptor D4 (e.g. by generalization). If we are interested in some "similar" case to I6 (referred to by D6) we can use this relation to infer case I1 and I5 (by D5). (2) Exploration across the boundaries of LGAs. We can investigate the relations between concepts of different LGAs, e.g. the concept "skeleton construction", belonging to the LGA "Construction" is related to the concept "grid" of the LGA "Geometry". In Fig. 1, the case I5 is referred by descriptor D5. We want to explore the consequences of this fact in the context of LGA 1. All cases with descriptor D5 have to be retrieved and searched for a link to a descriptor of LGA 1. If one descriptor within this resulting set of LGA 1 descriptors predominates, we can infer a relation between these descriptors. If we apply this strategy to descriptor D5, we will find descriptor D2.
4.2 Exploration using browsing strategies Browsing is an effective alternative to searching as an information strategy for ill-defined problems. During exploration the user consults nodes that have a high likelihood to be of relevance. Cues and hints support the user in her investigations. Browsing also can be purely random, thus finding relevant information by change. [Carm92] gives an introduction to cognitive aspects of the browsing activity. Within our information structure, the user can browse through information as well as descriptor nodes in a seamless way. 4.3 Exploration using both strategies A combination of both strategies seems to be a promising approach for an effective and efficient retrieval process. The user should simply indicate a case he is interested in which he found during some browsing activity. The system takes this case as a hint and presents semantic related cases arranged in decreasing order of relevance. Also semantic related descriptors are explored. Figure 2 gives an example [Kühn93]. We use two architectural textbooks, Precedents in Architecture [Clar85] and Logic of Form [Torr61] to extract two possible LGAs. The numbers (1) to (11) order the exploration steps chronological.
Using our information structure in a design aiding system, the designer may start with the question: How to design a widespanned roof sheltering a gymnasium on a rectangular site. After retrieving some design cases described by the concept "Roof" of the LGA "Construction" (1-3), s(he) gets interested in an example of a dome-shaped roof (4). Because s(he) wants to know the consequences of a dome-shaped roof for the ground plan, s(he) seeks for a corresponding example in the LGA "Formative Idea". The descriptor in the LGA "Construction" for the selected roof is "Dome" (5). The system infers a relationship to the concept "Concentric" of the LGA "Formative Idea" by using exploration across the boundaries of a LGA (6). More examples retrieved for "Concentric" prove to be inconsistent with the rectangular site (7). To find similar solutions s(he) may explore within the LGA finding "Double Center" (8) as related term concerning "Enclosure". Examples (9) prove to be suitable for a rectangular site, e.g. two domes. By even more relaxing the constraints and looking for similar "Configuration Patterns" s(he) would find the concept "Binuclear" (10) and an example, showing the addition of two related elements (11). The results of the exploration process are a list of examples, associated with a set of requirements. Thus starting from an initial set of requirements, the designer gained new experiences during the exploration process and ended up with a refined set of requirements, which will be closer to the description of the final design. 5 Conclusions We presented the model of a LGA as means for structuring architectural design cases. A LGA consists of precedent cases, indexing vocabulary, and the various relations within these objects. This model has been used to infer a knowledge structure for architectural design knowledge. We use the resulting knowledge structure in a design-aiding system, which proposes relevant cases (or parts of cases) in response to some query. Our emphasis lies on the design of an intelligent retrieval algorithm to filter relevant cases. We gave examples of desired exploration strategies. Exploration stands in contrast with search as paradigm for designing. Exploration focuses on the refinement of requirements rather than on the a priori definition of goal states. We use a query to define our information needs. The desired output of our system is a sorted list of cases, associated with a list of corresponding requirements. The following issues have to be considered in the future work: (a) which set of relations can be used in a semantic network to model the structure and content of the knowledge base; (b) which topology restrictions exist on the network; (c) which retrieval model incorporates uncertainty best. References [Arch87] J. Archea, 1987. ‘Puzzle-making: What architects do when no one is looking’. in Kalay, Y. E. (ed) Computability of Design. John Wiley & Sons, Inc., New York, NY
[Carm92] E. Carmel, S. Crawford,and H. Chen, 1992. 'Browsing in Hypertext: A Cognitive Study'. in: IEEE Transactions on Systems, Man, Cybernetics, Vol. 22, No. 5, pp. 865-883 [Clar85] H. C. Clark and M. Pause, 1985. Precedents in Architecture. Van Nostrand Reinhold Company, Inc., New York, NY [Crof89] W.B. Croft and H.Turtle, 1989. 'A Retrieval Model Incorporating Hypertext Links'. in: Proceedings Hypertext 89, Pittsburgh, pp.213-224 [Fris88] M.E. Frisse, 1988, 'Searching for Information in a Hypertext Medical Handbook'. in: Communication of the ACM, Vol.31, No.7, pp.880-886 [Kühn91] C. Kühn and M. Herzog, 1991. ’A Language Game Approach to Architectural Typology’. in Pittioni, G. E. (ed) Proceedings of the ECAADE 1991, München, BRD [Kühn93] C. Kühn and M. Herzog, 1993. 'Representing Architectural Design Cases'. in: Automation in Construction , 2 (1993), Elsevier Science Pub., Amsterdam, NL, pp.1-10 [Luca90] D. Lucarella, 1990. 'A Model for Hypertext-Based Information Retrieval'. in: N. Streitz, A. Rizk, and J. Andrù (ed) Proceedings of the First European Conference on Hypertext, INRIA, France, pp. 81-94 [Megh91] Meghini, C., Rabitti, F., Thanos, C. 1991. ‘Conceptual Modeling of Multimedia Documents’ in: IEEE Computer October 1991, Vol. 24, No. 10, pp. 23 – 30. [Newe72] Newell, A. & Simon, H. A. 1972. Human Problem Solving. Prentice-Hall; Englewood Cliffs [Quil68] M.R. Quillian, 1968. 'Semantic Memory'. in: Semantic Information Processing, MIT Press, Cambridge, MA [Salt83] G. Salton and M.J. McGill, 1983. Introduction to Modern Information Retrieval, McGraw-Hill, NY [Smit90] Smithers, T. & Troxell, W. 1990. ’Design is intelligent behaviour, but what is the formalism?’ in: AI EIDAM, Vol. 4, No. 2, pp. 89-98 [Torr61] Torroja, E. 1961. Logik der Form. Verlag Georg D. W. Callwey, München, BRD [Witt52] Wittgenstein, L. 1952. Philosophische Untersuchungen. Cambridge, England, 1952, reprint in: Wittgenstein, Werkausgabe Bd.I. Tractatus logicophilosophicus. Tagebücher 1914-1916 [u.a.], SuhrkampTaschenbuch Wissenschaft; 501, Frankfurt am Main, BRD [Vidl77] Vidler, A. 1977. ’The Idea of Type: The Transforamtion of the Academic Ideal, 1750 - 1830’. in Oppositions No.8 - 1977
