case-based design and creativity

Automation in Construction 2 (1993) 11-19 11 Elsevier

Case-Based Design and creativity

Gerha rd Schmitt

ETH Ziirich, Ziirich, Switzerland

Design reasoning with and based on architectural cases has a long history in architecture. Case-Based Reasoning (CBR) is a recently established research direction in Computer Science and Artificial Intelligence. Implementation of the intuitive Case-Based Design (CBD) method in architecture with AI techniques promises major advantages over traditional computer-based design methods. With CBD, an architect does not have to generate the building from scratch everytime a new design problem arises. Instead, positive and negative experiences from the past can be readily used. Starting with a case base of different buildings and building types, CBD can solve new design problems by adapting, modifying or combining existing cases. CBD does not limit the solutions to routine architecture but also allows innovative and creative design. There still exist major challenges in the representation and manipulation of complete cases which contain structured information (e.g., CAD models) and unstructured data (e.g., images, user responses, performance data). The paper will present first results of a research project in architectural CBD and then address the important issue of creativity. While CBD is in essence an effective method to limit the search space in design, creativity does make use of the entirety of the architectural search space. How, then, can CBD lead to creative design? We will introduce extensive case adaptation and case combination as possible answers.

Keywords: Case-Based Reasoning; design cases; Case-Based Design; creativity.

1. Introduction

Archi tec tura l design is the art of p roduc ing a comple te building specification f rom an incomplete p roblem description. We claim that in the process architects do reason with architectural cases. Number , complexity and sophistication of the cases increase with the experience of the designer. While we partially deduce these find- ings f rom observing architects and designers [1], the compute r science and AI communi ty has be- gun to implement systems to simulate the process of Case-Based Reason ing (CBR). C B R assumes the existence o f a collection o f selected cases, represen ted in complete and discrete form. We choose cases based on their architectural quality. C B R fur ther assumes that, given a new design problem, an existing case can be found and adap ted as a solution to the new problem without losing the inherent quality. The advantages are that (i) problems of generat ive approaches such as combinator ia l explosions may be avoided, (ii) the non-decomposi t ional i ty p roblem of architec-

* Discussion is open until November 1993 (please submit your discussion paper to the Editors on Architecture and Engineering, G. Smeltzer and H. Wagter).

tural design can be solved, (iii) the trade-offs in the cases are preserved, and (iv) the complexity barr ier can be broken. In the context of design, Rosenman , Gero and Oxman [2] a t tempt to for- malize the content of architectural cases. Shih uses C B R for architectural case adapta t ion [3].

Case-Based Design (CBD) is a fundamenta l pa rad igm in architecture. Only recently, com- pu te r science and AI research have provided the necessary techniques to realize non-trivial computer implementat ions. Ideally, a designer will be able to adapt a previous design to an new environment or a new functional descript ion without recreat ing the entire model . Case adapta t ion is feature-preserving but not necessarily geometry- preserving. The successful implementa t ion of such a system will for the first t ime take advantage of existing architectural , functional, geometr ic and o ther formal ized or uns t ruc tured knowledge combined in one building and will allow the cont inu- ous construct ion of a well founded body of architectural knowledge in the form of comple te and indexed cases.

In a three year research project sponsored by the Swiss Nat ionalfonds, we have developed an archi tecture or iented C B D approach and applied it to con tempora ry designs o f the Ticino archi-

0926-5805/93/$06.00 © 1993 - Elsevier Science Publishers B.V. All rights reserved

12 G. Schmitt / Case-based design and creaticity

tects Campi and Pessina. The first case adaptation and case modification experiments which include the architectural and the structural properties of a building are promising: they show that the results of CBR arc comparable to traditional design decisions [4]. After the definition of Case- Based Reasoning, Case-Based Design, creativity, design cases, and design reasoning, we will de- scribe case adaptation and case combination, two major methods for using cases in design. The paper will end with the presentation of ACABAS, the first implementation of our architectural CBR system [5] and reflections on the legal and ethical problems of CBD.

plications require efficient storage and retrieval but minimal adaptation mechanisms. Applica- tions in medicine are only useful if they contain a large number of cases and a consistent treatment and success pattern. Automobile repair CBR systems have been used for years in the form of symptom-solution descriptions which allow a minimum of variation.

Architectural design based on CBR places more emphasis on the adaptation and modification process rather than on storage and retrieval problems. The reason is that a single design problem can have many solutions whereas a flat tyre - - t o use an example from the automobile envi- r o n m e n t - h a s exactly one solution.

2. Case Based Reasoning (CBR)

Case Based Reasoning (CBR) originates from ideas on memory structure with cognitive and psychological motivations. CBR is a defined paradigm in artificial intelligence [6]. It assumes that humans reason from specific experiences rather than by following a set of general guide- lines. CBR relates the present situation to the closest experience in memory and uses that experience to solve an existing problem. It places emphasis on memory rather than on computation by retrieving instead of computing solutions. Key activities in CBR are the storage and the representation of cases as complete patterns, the re- minding of the most appropriate case and the application of that case to the current situation. The retrieved case may either match the current situation exactly or it may need modification. New cases are produced as modification of existing cases or as new cases [2].

CBR is used extensively in everyday life [17]. For example, judgment based on precedents is an established practice in law. Experienced doctors store large numbers of patient histories (cases) in their memory and in written form. Repair of cars and equipment follows previously successful attempts. In general, it appears that CBR is useful whenever reasoning from first principles will not achieve the desired results, either because the situation is too complex or causal relations between problem and solution are not known. Dif- ferent disciplines place distinct emphasis on the CBR related activities of case storage, case index- ing, case retrieval, and case adaptation. Law ap-

3. Case Based Design (CBD)

Case-Based Design is a specific kind of CBR. It departs from a description of existing buildings or designs in the case base and terminates with the creation of complete building specifications. Case-Based Design systems such as A RCH IE [7], CADSYN [8] and CADET [9] use cases to generate new designs. Case-Based Design systems have the following properties: - A CBD system does not require a complete

domain model but can produce complete and complex designs based on a relatively small knowledge base.

- Design starts from complete cases, implicitly achieving tradeoffs between several functions. This avoids the problem of multi-criteria optimization.

- Applying the design history of existing cases can make design problem solving more efficient.

- Using cases as the source of knowledge allows learning by storing new cases.

The four major processes are those of case definition, case retrieval, case adaptation and case combination. As we have spent most of our research efforts on the latter two, they will be described in more detail.

4. Creativity

Creativity is the art of causing to exist original ideas or objects. The definition is open to inter-

G. Schmitt / Case-based design and creativity 13

pretations that range from the layman's view of creativity as a mystical activity to Schank's provocative statement that creativity is mechanical [10].

Typically, we apply fairly stringent criteria in judging creativity. In most cases we require an act to pass three tests before we call it creative. First, we must believe, that the act is original. Second, we must believe that it is valuable. And third, it must suggest to us that the person who performed the act has special mental abilities. For example, when we see what the person has done, we ask ourselves, 'how did she ever think of that?' or, 'how did he have the patience to work all that out?' [11].

The recent inflation in objects that are labeled creative has lead Bazon Brock to the statement 'only hairdressers are creative', meaning that any- thing different from the normal and the arbitrary is labeled as creative nowadays. His own interpre- tation of creativity, however, is not that of creat- ing something out of nothing, but as giving a new order to existing components [12]. John Gero defines routine, innovative and creative design and offers graphical explanations of the space in which creative solutions may be found [13]. Con- ferences on design and creativity have produced attempts of definition but few examples which would support a general agreement on what is creative and what is not.

There are similarities to the definition of good

Fig. 1. Courtyard of the Felder house by architects Campi-Pessina with a superimposed three-dimensional geometric model of one of the case adaptions for a smaller building site. Model and background can be matched dynamically for interactive evaluation.

14 G. Schmitt / Case-based design and creativity

design and the creation of good design. While most people are able to recognize good design, few are capable to execute it. With regard to Case-Based Design the question of creativity arises again. In order to measure the degree of creativity of the artifacts which we produce with CBD, here is an attempt for formalization [14]: - Architectural creativity requires knowledge of

a set of precedents. In a computer environment, that corresponds to an intelligent database of existing buildings and building elements, organized by index. This condition can be met by indexed case bases or object oriented databases.

- Architectural creativity is a function of the designer's ability to explain the precedents and their reasons for being in the database. In computer terms, this refers to the capacity of programs to perform abductive inference or explanation.

- Architectural creativity relies on heuristics to find applicable solutions of the past and to adapt them to new design problems. In computer terms this relates to heuristic search and inductive reasoning or to the selection of the appropriate case in a case base.

- Architectural creativity builds on the capacity of the designer or an external critic to ask harsh, uncomfortable and seemingly unrelated questions. In computer terms, this capability has no equivalent yet. It is related to abductive and inductive reasoning and is almost the in- verse of deductive inference. Questions allow designers to view the problem in a larger context. Until software will be able to reason with common sense knowledge, computers will not be able to perform this task.

- Architectural creativity is a function of the idiosyncratic experiences of individual designers. In computer terms, this will mean the programming of individual case bases and the interaction of many programs with access to different case bases.

Whereas the first three criteria show that the capability to learn, remember, and apply knowledge are necessary to be creative, the last two criteria demonstrate the necessity of questioning and individual discovery. Creativity definitely builds on extensive knowledge. To produce an object that one has never designed before but which exists in similar form already might be a

creative act for a specific designer but does not meet the criteria of creativity that we will use from now on.

A few of the cases produced by our emerging CBD system could be labeled creative according to the criteria outlined above. This was more a by-product of our investigation as the main focus was on adaptation mechanisms (Fig. 1).

5 . D e s i g n c a s e s

Design based on first principles is practically non-existent. There is no formula or collection of algorithms that leads from the design problem definition to a complete design solution. As long as there is no formalized definition of architectural quality, there will be no mapping between design problem and design solution. Conse- quently, traditional architectural design education makes extensive use of architectural cases. Architects use them as examples, represented in the form of slides, drawings, models, and verbal descriptions. More important, students can visit the buildings and experience the relation between the abstract representations and the building itself. Thus, they begin to develop a mapping between representation and reality. We define the knowledge that architects acquire in this manner as design knowledge which is necessary to synthesize a new design from incomplete information.

A second most important component of architectural design education is the teaching of skills to analyze existing and new designs. We define the knowledge necessary to do so as domain knowledge. Domain knowledge is used to analyze the proportions and the structural safety of a design, to execute cost calculations, to perform energy analysis, and to judge the performance of a building based on multiple criteria.

It is possible to use domain knowledge to generate design by programming generative systems that create solutions within the limits of the domain. It is thinkable to have a machine create a building by only applying building code restric- tions, fire safety laws, structural safety codes, as well as cost and energy constraints. The result would probably be disastrous because domain knowledge is normally used to prohibit undesired consequences and to analyze existing structures.


There is with few exceptions no formalized domain knowledge about architectural quality [15]. On the other hand, buildings created using only design knowledge usually do not fulfil all analysis criteria described with domain knowledge. An interactive process of design and analysis gradu- ally moves design to a balanced status.

A case base is the collection of cases in structured and unstructured representation. Struc- tured case representations include CAD models particularly constructed for the purpose of CBR. We have programmed a pre-processor (Mod-4) for this purpose. The pre-processor accepts the geometric description of a building and requests room labels, material descriptions and structural design specifications. Based on this input, Mod-4 produces an object database and a graph description of the building. All design reasoning must work with these representations. A second type of information in the case base is unstructured case information. This includes scanned images, text descriptions of the building, interviews with occupants, energy cost bills, data about employee absence, acoustical and thermal problem areas, repair history and similar information. Although we might not know the relation between the architecture and location of a building and these factors, the complete collection of information in the case base might help to discover causal relations. As the storage for both types of information, an object oriented database appears best suited. The database must also support binary large objects (BLOBs) to relate the structured and unstructured representations and to effec- tively integrate graphical and alphanumerical information.

Ideally, an architect has access to a case base at the beginning of the career. Over the years, an own language develops and own cases become the main resource for new designs. It is therefore important that weighting factors can be applied to steer the selection of features and the influ- ence of past cases on new design.

6. Design reasoning

Design is an activity that requires a high degree of intelligence: it cannot be done based on first principles, it should not be generated from domain knowledge only, it should have all the

qualities that we have come to like but it should also communicate a degree of excitement, innovation, and creativity. Faced with these challenges and in the absence of sufficient experience, architectural students tend to 're-invent the wheel' many times, whereas experienced designers, due to lack of design time and overload from administrative and acquisition tasks often rely on previous solutions or on sudden intuition. We claim that CBD can help to improve this situation by providing an overview of excellent cases from the past for specific design problems and by mak- ing them available for design reasoning.

We assume that cases are a major source of design knowledge. We also assume that cases contain all relational knowledge important for a design and that a case is almost optimal for the purpose it was constructed (otherwise it will not become part of the case base or it will appear in the section of cases to avoid). In a first step, it therefore does not need further optimization.

7. Case adaptation

After the definition of a new design problem, an architectural CBD system will first search for the closest solution to a similar problem in the past and will then attempt to adapt the selected case to solve the problem at hand. Applying an existing architectural case to a new problem has the following advantages: - The designer is not forced to recreate a com-

plete architectural description from scratch. All attempts to automate the design process from scratch have failed so far, mainly because of the complexity barrier that occurs once other than trivial design problems are present. More successful are prototype refinement approaches which offer acceptable solutions to well defined routine design problems [16].

- If a suitable case is found, the quality of the existing case will be maintained in the adapted case as well. CBD does at least offer this possibility, although even the slightest geometric change in an architectural model can cause significant quality differences. Except for the null adaptation, that is, when a case can be applied to a new situation without any changes, every adaptation will change the character of the original case. The severity of the change

16 G. Schmitt / Case-based design and creativity

determines the distance in structure, behavior and geometry from the original case.

- Case adaptation does occur at distinct levels. So far, we have achieved automatic adaptations at the geometric and the topological level. Faced with a design problem, our system first a t tempts to adapt the selected case to the new situation by changing the geometry. If this is unsuccessful or if proport ional and other geometric constraints are violated, the system switches to a topological adaptation. The re- suits of the topological changes are then ex- pressed geometrically.

- Case adaptat ion in our implementat ion does not require a complete parameter izat ion of the building model. This differentiates it from prototype refinement where all parameters must be known beforehand. Relevant parameters are defined in the process of adaptation. The process of dimensionality reduction [4] reduces a possibly large number of parameters to a feasible number.

In case adaptation, the question of copyright and other legal problems may arise. I f a designer uses a case base of well known architects and adapts their masterpieces, a wave of law suits could be the result. On the other hand, if case adaptat ion is applied to partial design solutions, the problem may not be as severe. Nobody can claim to be the patent holder of the rectangular room or the arched window, yet it is possible that mass production of slightly adapted Botta, Rossi, or Eisen- man buildings will rightfully cause problems. We therefore repeat the proposal that architects develop their own case base.

8. Case combination

Besides case adaptat ion which is in the implementat ion stage we consider case combination as the next logical step. Case combination assumes that an existing case can not be adapted because differences between source and target are too large. The approach is somewhat similar to prototype combination which, according to Gero, can lead to innovative design. Case combination touches upon the importance of analogy in architecture. With case combination, features from radically different cases may be combined to form a new and definitively different design solution.

However, the original quality of either of the two or more cases can not be guaranteed. For case combination, we are in the experimental stage.

Case combination becomes necessary when neither geometrical nor topological adaptation of the selected case were successful. In case combination, the architectural distance of the new object f rom the selected cases is larger than in case adaptation, therefore the maintenance of the original quality is guaranteed even less. Case combination allows the combination of the best features of different cases to solve a design problem for which case adaptation is not feasible. With case combination, it is easier to achieve innovative design solutions than in case adaptation which will normally produce solutions to routine design problems only.

Case combination also poses less of a possible legal problem. Architects do use knowingly or unknowingly architectural components that appear in identical or only slightly different form in previously designed buildings. However, the di- mension of the architectural search space assures that most designs are different. The extent of the search space is a main problem for computation in architecture, and CBD is one effective method to reduce the search space. As a result, there could be more identical parts or combination of parts in buildings designed with CBD systems.

9. ACABAS--Architectural Case Based Design system

We started research on CBD in 1989 and implemented a first prototype of an 'Architect- ural CAse BAsed design System' (ACABAS) in 1990 to prove the validity of the approach. Since then, the original system has been improved and changed several times and does now include architectural and structural reasoning modules. It demonstrates the importance of visual feedback for the evaluation of the adapted cases and dis- tinguishes between appropriate levels of abstraction in an architectural design development. A- CABAS also proves that search can be reduced to a minimum and that the adaptation of buildings with several hundred elements including spaces, walls and openings is feasible on existing workstations. The ACABAS system performs the following tasks:


1. Evaluation of the existing case in the original and new environment in order to find topological and dimensional discrepancies.

2. If there are dimensional discrepancies, ACA- BAS identifies the violations and defines a set of constraints on the local generalization of the design. It adds these constraints to the constraint list stored in the case. Normative constraints and functional constraints which contain inequalities are kept separate.

3. If there are topological discrepancies, ACA- BAS calls corresponding transformation rules to t ransform the topological graph of the case to meet needs of the new site while preserving the building functions. It rebuilds the dimensional constraints after topological transforma- tions.

4. Using constraints which are not satisfied in the new environment and constraints in local gen-

eralizations of the design, ACABAS defines all the variables in this local constraint net- work with adaptation parameters through a domain independent dimensionality reduction process.

5. ACABAS varies the adaptat ion parameters in order to ensure that no constraints are violated. It propagates changes to related variables inside relevant areas of the building.

6. ACABAS checks the validity of adaptation by verification according to the constraints that are not included in the dimensionality reduction, such as those constraints which are ex- pressed in terms of inequalities. If no constraints are violated, it terminates the process successfully. If some constraints are violated, it proceeds to the next step.

7. If constraints are not maintained in the new design proposal, ACABAS triggers topological

A C A B A S S y s t e m O v e r v i e w i C o m m o n Lisp A u t o C A D + A u t o L i s p

Architectural adaptation processor (frame based representation)

- Case insertion - Case evaluation - Dimensional adaptation - Topological adaptation - Output to Structural adaptation processor

Case library AutoCAD Models

Structural adaptation processor (frame based representation)

- Evaluation in original context

- Adaptation of structure to suit architectural adaptation

User input and display (graph based representation)

- Input of design elements - Generation of a graph based design representation

- Output to the architectural adaptation processor

Fig. 2. System overview of the existing ACABAS implementation with case input, case library, and case adaption processors.

18 G. Schmitt / Case-based design and creatiL~ity

transformation rules which relax constraints in the related constraint set. If there is a transformation which preserves design features of the case, it returns to step 1, otherwise the case is not suitable.

ACABAS has been tested on residential buildings and on general apartment floor plans. The adaptations it suggests are also checked for their structural validity. The performance of the system does not degrade exponentially when more elements are introduced for parameterization. The time it takes to adapt a design with 200 elements is a linear development from the time it takes to adapt a design with 60 elements. The system leaves the judgement of wether this type of computer aided architectural design is creative or not directly to the architect and the client, without forcing them to make decisions on abstract representations such as diagrams, performance charts, or cost analysis alone. Rather, these techniques will be used to guide the geometric and topological adaptation of the cases. Three-dimensional models, generated from existing cases and react- ing to a new design problem demonstrate some relations between innovation and creativity and the degree of deviation of the adapted or modified case from the original case.

ACABAS is implemented in Lisp and Au- toLisp and uses AutoCAD as the graphic engine. Computing platform is the SparcStation 2. For testing the results of the adaptation process, we transfer the models to our own 'Stalker' visualiza- tion software on Silicon Graphics machines (Fig. 2),

10. Ethical and legal aspects of Case-Based De- sign

In legal terms, reasoning with design cases might open up a new discussion on copyright and related issues. In design education, the worst case scenario is that reasoning with cases might cause plagiarism and the inappropriate combination of elements.

In the United States and in Switzerland, for example, descriptions of building elements are made available by manufacturers in traditional and electronic media. An architect who selects an element from such a database is aware of the producer and the guaranties and disclaimers con-

nected with the product. Use of these elements is based on the consent of the manufacturer. In the possible situation that a successful design by a particular architect is the starting point for case adaptation, the consent of the architect normally does not exist. It is therefore thinkable that architects will take legal action against the production of slightly modified clones of their design. The question arises in each country to which degree an architectural idea can reach copyright status. In this case, royalties would have to be paid to architects as well. On the other hand, it has been a tradition in the United States and other coun- tries to purchase complete construction drawings of residences for a minimal fee and to construct the building accordingly.

In education, Case-Based Design has stronger ethical aspects. Although students extensively uti- lize precedents in architecture, the traditional 'manual ' design almost guarantees that a previous solution will not be copied exactly. If design precedents become available in complete structured and unstructured representations in the form of cases, the danger of copying entire projects or parts of projects increases. Students may also not understand the inherent quality of selected cases because they are not forced to re- build them from scratch. It thus appears that CBD is more appropriate for upper level design students than for beginners who should concen- trate on bottom up design methods such as gram- mar based design. As a positive effect, the extensive possibilities for comparison of existing architectural solutions with the design problem at hand might lead to more responsible solutions and to a better founded definition of creativity in design.

11. Conclusions

We are aware of several potential dangers of the CBD approach. There is the mechanical nature of case adaptation and case modification: If the designer adapts cases of other architects, plagiarism and potential legal consequences are possible. This problem can not be easily dis- carded and is of ethical nature. There is the argument that adaptation is not a creative process: Applied without intelligence, case adaptation could lead to an architecture of average


without improvements of the current situation. There are computational challenges to be solved in CBD applied to architecture such as dimensionality reduction and automatic feature-preserving functions. But we are convinced that if case adaptation is expanded to case modification or even case combination, Schank's definition of creativity as a mechanical activity is fulfilled. We are even more convinced that without being based on an extensive body of case knowledge, architectural design will continue to be reinvented by the majority of architects who can not build on their own extensive experience. No other discipline can afford to operate this way with every artifact it designs.

Reasoning with and based on cases is a traditional and proven method in architectural design. Translating this process into a computing environment does offer major challenges: case representation, completeness and granularity are de- ciding factors for the success of the CBD process. Geometric and topological adaptation procedures transform the original case into a new design. This may result in creative solutions but a routine design solution is more probable. Case combination, however, will more likely lead to creative solutions but with less guarantee that the positive qualities of the original cases will be maintained. With a non-trivial prototype implementation we have proven that CBD could play a significant role in design and does not hinder creativity.

Acknowledgments

T h i s research is carried out in collaboration with Professor Boi Faltings, Dr. Ian Smith and Kefeng Hua at the Laboratoire d'Intelligence Ar- tificielle, EPF Lausanne, Simon Bailey, ICOM EPF Lausanne, and Shen-Guan Shih at the Pro- fessur fiir Architektur und CAAD, ETH Ziirich. Funding is provided by the Swiss Nationalfonds under the Program NFP 23: Artificial Intelli- gence and Robotics.

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