Representing design knowledge as a network of function, behaviour and structure

Representing design knowledge as a network of function, behaviour and structure

Min Yan, Research Institute of Engineering Mechanics, Dalian University of Technology, Dalian 116023, China

A memory-based representation for design knowledge is proposed to

support nonroutine and creative design, and to facilitate design knowledge

acquisition. It is a kind of network of function, behaviour and structure

(FBS-network), from which design prototypes appropriate for particular

design tasks are dynamically retrieved. Prototype retrieval, routine design

and nonroutine design based on the FBS-network are studied. Ways of

applying machine learning techniques on the FBS-network acquirition are

investigated.

Keywords: knowledge representation, design prototype, creative design,

machine learning

D esign is regarded as a transformation process from function to structure’. Goal-directed search cannot adequately characterize this transformation, since designers begin designing before all

the relevant information is available. In the design process, what is relevant only manifests itself as the design proceeds and varies with the decisions taken, so this transformation is a kind of exploration*.

1 Gem J S ‘Design prototypes: a knowledge representation schema for design’ Al Meg Vol 11 No 4 26-36 2 Smltham 1 et a/ ‘Design as intelligent behavior: an Al in design research programme’ Artifi- cial ln~efligence in Engng Vol 5 No 2 (1990) 78-108 3 Gem J Sand Rosenman M A ‘A conceptual Iramework for knowledge-based design research at Sydney University’s Design Computing Unit’ Artificial Infelligena, in Engng VoI 5 No 2 (1990) 363-982

What is the design knowledge that support it? Gero3 argues that human designers schematize their knowledge. Such a scheme consists of knowledge generalized from a set of similar design cases and forms a class from which individuals can be inferred. He proposes a conceptual schema called design prototype to provide a framework for storing and processing design knowledge. Design prototypes bring all the requisite knowledge appropriate to the design situation together in one schema, that provides a basis for the start and continuation of design.

A design prototype generalizes previous designs according to the four categories: function, behaviour, structure and their relations. The designer’s memory is assumed to be a base of design prototypes. Design with

314 0142-694X/93/030314-16 0 1993 Butterworth-Heinemann Ltd

4 Gem J S, Maher ML end Zhang W ‘Chunking structural design knowledge as prototypes’ in J S Gem (Ed), Atlificial /nW/i- genc.s in Engineering: Design Elsevier. Amsterdam. 3-21 5 Tham K W, Lea H Sand Gero J S ‘Building envelope design using design prototypes’ in A/ in Building Design: Pmgmss and Pm&x+. ASHRAE. Symposium. St. Louis. MO (1990) 6 Yan M The RECGRAPH theory for intelligent floor plans design, Doctoral Thesis, Huazhong University of Science and Technology, China (1990) 7 Dahlbach N (1989). A symbol is not a symbol, Pm. ol lllh IJCAI, pp 9-14

prototypes is a recursive process of selecting (or adapting or-generating) appropriate prototypes from the prototype base and refining them. As a prototype is selected, new requirements which were not manifest earlier are added to the systems. The process of selecting additional prototypes which produce increasing detail and additional requirements provides a basis for exploration in design.

However, implementations with prototypes are still limited to routine design . 495 Nonroutine design with prototypes needs further investigation before it can be implemented in a system. But, it is difficult to acquire prototypes. The prototype paradigm has the added difficulty of encoding sufficient knowledge to be a generalized template for the design of instances derived from it.

This paper pursues Gero’s paradigm by investigating these problems. It is elucidated that: in the designer’s memory design knowledge is not stored as a hierarchy of prototypes, but a network of function, behaviour and structure (FBS-network). Design prototypes for given design specifica- tions are dynamically retrieved and organized from the FBS-network. Two kinds of prototypes are retrieved from the FBS-network: the complete prototypes which provide all the requisite knowledge for the given design task, and the incomplete prototypes which do not provide all the requisite knowledge. Thus routine design is defined as design with complete prototypes, and nonroutine is design with incomplete prototypes. In this paper, prototype retrieval, routine and nonroutine design are studied in the FBS-network. Machine learning techniques are introduced to the FBS-network acquisition to overcome the difficulty of design knowledge acquisition. The ideas discussed here have been partially studied experimentally, in a prototypical system RECHOUSE which designs house floor plan@.

1 FBS-network representation for design knowledge What is the representation for design knowledge? The answer to this question depends upon our understanding the word ‘representation’. In fact, it is used in two different senses’. In the narrow sense, representation can be limited to a symbol or a mental image which signifies an object. In the wider sense, representation is knowledge about the object or the relations of the object to others by which we understand the object. For example, an image of rabbits is the narrow sense representation of rabbits. The wider sense representation of rabbits includes: rabbits are mammals, rabbits eat clover, rabbits give birth to their young, dig burrows and run from foxes, etc. Normally, when we think of an object, both the narrow and the wider sense representations are recalled from our

Design representation 315

Figure I Funcrion-orienred

represenration

memory. The narrow sense representation is the symbolic evocation of the wider sense representation. Integrating both the narrow and the wider sense representations into one representational schema forms an object- oriented representation. According to the prototype hypothesis, design knowledge is composed of functions, behaviours and structures and their relations. Thus how to represent design knowledge depends upon how to represent each abstract object of function, behaviour and structure in an object-oriented representation.

Function The functions of a design are its actions or efficacy in some context which is composed of environmental constraints and human goals. The functions of a design are determined by its behaviours, structure, and existing environments and human goals.

We conventionally represent functions as human goals and constraints, so the narrow sense representation of functions includes human goals and constraints (in the following, functions will be referred to goals and constraints). The wider sense representation of a function includes its various relations to behaviours and structures. One function may demand many behaviours. One behaviour may be contained by many structures, one structure may be of many behaviours. Also there may be direct mappings from a function to structures. Thus the wide sense representation of a function is a function-oriented representation composed of behaviours and structures as illustrated in Figure 1, where the connecting edges represent the relations which can be simple links allowing direct access from one node to another, or transformational knowledge of the following kinds:

316 Design Studies Voll4 No 3 July 1993

Figure 2 Behaviour-orierrred

represenlntiorr

l From function to structure, that may be direct mappings l From function to behaviour, that characterizes design problem for-

mulation l From behaviour to structure, that may be generative knowledge used

in design synthesis l From structure to behaviour, that may be interpretive knowledge used

in design analysis

1.2 Behaviour Behaviours are the expected responses of a design in a design context, they can be determined by the structure and attributes of the structure of the design. Here, attributes refer to the properties that can be observed and measured by scientific methods.

When we think of a behaviour, we usually associate it with some structures which contain the behaviour. Also we usually associate it with some contexts in which the behaviour plays a role, i.e., we associate a behaviour with some functions. So in a behaviour-oriented representation, relevant functions, structures and their relations to the behaviour must be included (see Figure 2).

1.3 Structure The structure of a design is its spatial configuration which is determined by its elements and the spatial relations among the elements. The granularity of spatial relation is larger than that of geometric relations which are determined by geometric attributes, i.e., one spatial relation corresponds to many different geometric attributes. Thus a design structure is distinct from a concrete design description (e.g. a drawing) which is

8 Yen M and Cheng G D Image- based design model. Design Stu-

determined by concrete geometric attributes. A design structure is an dies Vol 13 No 1 (1992) 97-97 abstract analogy or an image’ of a group of design descriptions. However,


Figure 3 Smmure-orienfed

represelmion

the granularity of spatial relations is smaller than that of topological relations. Topological relations are independent of dimensions, while spatial relations are dependent upon dimensional constraints. Thus design structures cannot be fully represented by topological representations. Design descriptions having variables are one kind of the narrow sense representation of design structures. The variables may be structural variables, dimensional variables or attribute variables, they are constrained by the spatial relations embodied in the structure.

Once we focus on a structure, we associate some of its behaviours (identified by attributes), as well as some interpretive knowledge which translate structures into behaviours. Also we may associate some of its functions. So the wide sense representation of a design structure is composed of its various relations to some behaviours and functions as illustrated in Figure 3.

1.4 FBS-network: a memory-based representation for design knowledge Integrating the three representations of function-oriented, behaviour- oriented and structure-oriented into one schema, we obtain the FBS- network which is composed of functions, behaviours and structures as shown in Figure 4.

It is supposed that in the designer’s memory, knowledge is stored in the FBS-network form, although we are not aware of it. We are aware of prototypes, frames and rules, but awareness is itself a process which is influenced by our mood and health. What is the existing form of design knowledge before our retrieval? It is important to investigate the existing form of design knowledge before retrieval because: firstly, it may probably provide a basis for us to explain why and how a designer’s ability


is influenced by the state of his mood and health. When in a good state he is aware of abundant information and is likely to do more creative work. Secondly, it may probably provide a basis for us to study how appropriate knowledge is dynamically organized from our memory for nonroutine and creative design (e.g., how design prototypes are adapted and generated). Finally, it may probably facilitate design knowledge acquisition.

We define the memory-based representation as the representation for the existing form of knowledge before it is retrieved, and suggest the FBS-network as a memory-based representation for design knowledge.

The FBS-network can be decomposed into many function-oriented representations (cf. Figure 5). Since a prototype is a function-oriented representation, a FBS-network can be converted into a network composed of many prototypes (cf. Figure 6).

So we have two kinds of memory-based representations for design knowledge. The first is a network of functions, behaviours and structures as shown in Figure 4. The second is composed of prototypes, as shown in Figure 6. When the two representations are compared we find that: firstly, the second representation makes it easier than the first one for us to explain our design knowledge. Knowledge for a particular design domain is clustered as one MP (for example, a MP for housing design is different from a MP for machine design). According to the second representation, design knowledge of one domain is further clustered as prototypes according to functional requirements that coincide with our understanding of design as a goal-directed activity. However, the second representation is, in fact, a kind of post factum rationalization. We cannot foresee functions for all possible future design tasks. But, once the design knowledge in a MP is partitioned according to predetermined functions, it is difficult to use the MP to solve a design problem with functional


Figure 5 Om FBS-network decomposed into two prototypes

requirements that are not the predetermined ones. It is suggested that a designer’s knowledge for a particular design task is not directly retrieved from his memory, instead, the relevant knowledge is dynamically associated and organized into prototypes. The first representation provides the flexibility of retrieving design knowledge dynamically for various different design tasks (cf. Figure 7).

Finally the second representation implies that design knowledge should be acquired by first constructing an individual P, then connecting the prototypes into a network. In this way, the difficulty of prototype acquisition still exists as was noted in the introduction. While the FBS-network can be incrementally constructed with functions, behaviours and structures, there is no need to encode sufficient knowledge to be a generalized template. This supports the application of machine learning techniques to design knowledge acquisition.

2R outine design Routine design involves two kinds of operations: prototype retrieval and prototype refinement. Prototype retrieval involves activating and selecting, from a FBS-network, some functions, behaviours and structures, and organizing them into a prototype. The newly retrieved prototype will

Figure 6 FBS-network repre-

sented m (I network of pro-

totypes. P. prototype; MP.

FBS-network; two Ps my be

connected by either their

structures or behaviours


Figure 7 Differenr prorotype

can be dynamically orga-

nized and retrieved from one

FBS-network

provide design knowledge for the current design task. Prototype refinement involves instantiating the retrieved prototype into design descriptions.

We call the nodes (in terms of function, behaviour and structure) in a FBS-network, which represent design requirements, the initial nodes. An algorithm for prototype retrieval is constructed from the two steps:

1) Find the initial nodes in the FBS-network, and activate them 2) Successively activate new nodes which relate to the old activated

nodes, until no new node can be activated. Here the new nodes to be activated can only be l Behaviours linked by activated functions l Structures linked by activated functions l Structures linked by activated behaviours

Figure 8 shows a FBS-network. Fl is assumed to be the requirements of the current design task. Applying the retrieval algorithm to Figure 8(u), a design prototype as in Figure 8(b) specially for the current task is retrieved. Note that once two nodes are activiated, the connecting edges of the two nodes are also activated.

If the retrieved prototype does not contain a structure node which is


a b

Figure 8 Prototypes b and c retrieval from a

linked by all the initial nodes, or the requisite behaviours required by the initial nodes, the retrieval for the requirements fails and the retrieved prototype is incomplete. Figure 8(c) shows an incomplete prototype for FO which requires that both BO and Bl are simultaneously satisfied within one structure. An incomplete prototype implies that there is no existing design structure or generative knowledge which is readily available for prototype refinement. Generally, an incomplete prototype does not provide all the requisite knowledge for given design task, while a complete prototype does. Design with an incomplete prototype needs new structures to be produced before prototype refinement. As new structures are introduced into the design, new variables of structures, behaviours and functions are introduced into the design. That is characterized as nonroutine and creative design’.

3 Nonroutine design Nonroutine design takes place when routine design fails. Routine design may fail either in the process of retrieval, or in the process of refinement. So there are two kinds of nonroutine designs: nonroutine design which comes from refinement failure, and nonroutine design which comes from retrieval failure. Based upon the FBS-network, the following three operations are available for nonroutine design.

l Operation (1): to activate new nodes which are not included in the current prototype. Here unlike the algorithm for prototype retrieval, there is no restriction on new node selection.

l Operation (2): to use the knowledge included in the new activiated nodes to construct new structures.

9 Gem J S and Msher M L l Operation (3): to interpret the new structures to see if they satisfy the ‘Mutation and analogy to support neativity in computer-aided de-

requirements of the design task.

sign’ Working Paper, Depatlment of Architectural and Design Sci- ence, University 01 Sydney. Au-

3.1 N onroutine design arising from refinement failure stralia (1991) As was noted earlier, a design structure can be represented as a variable

322 Design Studies Vol14 No 3 July 1993

so:

BO :

p--I-+-pi-I

room’s length is too long

Figure 9 Example of nonroutine design

design description with variables of dimensions, attributes and structural elements. The variables are constrained within some ranges which are determined by the spatial relations embodied in the structure. Prototype refinement involves instantiating a prototype into concrete design descriptions. It involves determining values for each variable of the design structure. The values of the variables are determined to satisfy the required functions and behaviours.

When the refinement fails, no satisfactory values can be found within their constrained ranges. To satisfy the requirements some variables have to be given’values from outside their constrained ranges, this implies that some spatial relations of the structure may be damaged and some behaviours based upon the spatial relations may be cancelled. In this case, nonroutine design is to recover the cancelled behaviours by modifying the design structure.

Figure 9 shows a simple example of this kind of nonroutine design. Where Bl and Sl are included in the retrieved prototype, Bl is a requirement of floor area of 70 m’, Sl is a structure of floor plans with a constraint that each room’s length should be from 2.6 m to 5.1 m. In the refining process, Sl is instantiated and the length of the bedroom becomes 7.2 m, this destroying the constraint of Sl. In this case, nonroutine design proceeds by associating BO and SO, followed by applying the rule contained in SO to change Sl into Sl’.

3.2 N onroutine design arising from retrieval failure Nonroutine design arising from retrieval failure aims to design with incomplete prototypes. One approach for this kind of nonroutine design is


analogical reasoning’. The analogical reasoning approach can be classified into two categories: derivational analogy and transformational analogy. Derivational analogy applies the past problem solving processes or methods to solve the new problem. Transformational analogy adapts the solutions to the past problems for the new problem. The FBS-network supports both transformational and derivational analogy.

In the FBS-network, particular prototypes are not predetermined. They are dynamically formed according to particular design tasks. An incomplete prototype contains no readily available structures, but it contains the structures which have similarities in terms of behaviours and functions (e.g. in Figure 10, S2 and S3 are ‘similar’ since they all relate to B3). The ‘similar’ structures are potentially available for transformational analogy: new structures can be generated by adapting one to another. ‘Similar’ structures can be associated from the incomplete prototype (in Figure 10, SO and S4 are associated). Furthermore some functions and behaviours from the incomplete prototype may also be associated, implying that new prototypes are retrieved to be used for transformational analogy. For example in Figure 10, the newly retrieved F3, B4 and S4 form a new prototype. The new prototype with F3 and the old prototype with Fl and F2 have similarities in terms of Bl and B3.

If design steps and design strategies are stored as behaviour in the FBS-network, new structures can be generated by using the ‘similar’ behaviours representing design steps and strategies, that is derivational analogy. An example of derivational analogy (see Figure 10) is to use both the strategy or design steps contained in BO and BS to generate a new structure.

4 The acquisition of a FBS-network We now investigate how to apply machine learning techniques to the acquisition of a FBS-network. The following characteristics of a human designer’s learning activities are observed:

l Two important sources from which design knowledge come are past designs and design activities

l Design knowledge is learned by generalizing from the sources, the generalization is an incremental process: design knowledge evolves from the sources

l In the process of generalization, analytical knowledge is used to interpret designs

324

Based on these observations, we construct a simplified learning model as

Design Studies Vol14 No 3 July 1993

/ --\ Incomplete -

prototype

Figure IO Design wirh

complete protorypes

in-

shown in Figure 11. In this model, learning is considered to be integral with the design activity, where ‘design activities’ include past design activities and current design activities. Past design activities are recorded in the designer’s protocols and explanations. The knowledge about design steps and design strategies is generalized from design activities. ‘Designs’ mainly refers to design structures and design descriptions.

‘Analytical knowledge’ is used to interpret designs. The ‘generalizing’ module is composed of a set of operations. Some operations are:

l To memorize a design or a design process l To interpret a design l To classify designs and design processes according to the interpreta-

tions l To find causal relations by discriminating the interpretations l To generate new concepts by induction l To use some heuristic rules to generate concepts and relations

When applying this learning model to the FBS-network acquisition, the following stipulations are available: firstly, a design case is defined as a design structure, or a design action, or a sequence of design actions; and secondly, a FBS-network is an index system of design cases, the indices are in terms of functions and behaviours (design strategies, generators and interpreters are considered as behaviours).

According to the learning model, the acquisition of a FBS-network is an


Figure I1 Learning model

for a designer

10 Coyne R D. Roeenman MA, Radford A D, Balachan- dran M and Gero JS Knowledge-Based Design SyS- terns. Addison-Wesley (1990)

Generalizing - d Design knowledge

incremental transformation from design cases to an index system of the design cases as illustrated in Figure 12.

The three operations for the acquisition of a FBS-network are as follows

1) To interpret a case 2) To catalogue cases 3) To generalize indices

The first operation is a kind of analysis where properties implicitly contained in design cases are interpreted. In the second operation, design cases are linked to their interpreted properties as indices. If a newly interpreted property did not previously exist in the index system, a new index indicating the property is built. The third operation is to further construct the index system by building relations among the indices. Some methods for the third operation have been introduced by Coyne et al”. In the following, we illustrate how to use set operation to generalize indices. Figure 13 shows a simple index system with 10 cases (1, 2, . . ., 10) and 5 indices (A,B,C,D,E). One rule for generalizing indices is as follows (11, 12 and 13 are sets of cases):

IF I1 u I2 = 11 THEN 12 = (11)~ (12-11)

We apply this rule to the index system as follows:

Figure 12 Evolution from design cases to II FBS-network


Indices

Cases 1 2 3 4 5 6 7 8 9 10

B= (1.2.3)

A= ~1,2,3,4,5,6,7,8}

C= (4,5,6)

D= (7.8.9)

E = f7,8,1Oj

Figure 13 Simple index system

.: B u A = A :. A = {B} u (A-B ) = (B, 4, 5, 6, 7, 8) .: C u A = A :. A = {C} n (A-C) = {B, C, 7, 8)

so the index system shown in Figure 13 can be converted into that in Figure 14(a). Again:

‘: (DnE) n A = A :. A = {(DnE)} u (A-(DUE)} = {B, C, (DnE)}

hence, Figure 14 (a) becomes Figure 14 (b). The relations of the indices implicitly contained in Figure 13 are now explicitly represented in Figure 14(c). For example in house floor layout design, let A stand for a requirement of usable floor area from 55 m* to 80 m2; B,C,D, and E stand for different household types:

B 3 bedrooms and 1 living room C 2 bedrooms and 1 living room and 1 dining room D 2 bedrooms and 1 living room E 2 large balconies

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

a b Figure 14


Figure 15

11 Maher M L ‘Process models lor design synthesis’ A/ Meg Vol 11 No 4 (1990) 49-M 12 Goal, A K and Chand- rasekaran, B ‘Use ot device models in adaptation of design cases’, Proc DARPA Case- Based Reasoning Workshop, Pansarola, Ptorida. USA (1991) loo-109

*1 *2

G(B)

IN

F

0 D E

1

11

From Figure 14, we can know that there are 3 possible ways to design an apartment with a usable area from 55 m* to 80 m*:

1) To design an apartment with 3 bedrooms and 1 living room 2) To design an apartment with 2 bedrooms, 1 living room and 1 dining

room 3) To design an apartment with 2 bedrooms, 1 living room and 2 large

balconies

The learning process can be accelerated by: (a), directly inputting relations into the index system (e.g., when the relations in Figure 15 are input into the index system, Figure 14 (b) evolves to Figure 16); (b), improving the ability of the generalizing module; and (c), improving the analytical knowledge.

Initially the index system of design cases may not be generalized sufficiently so that design prototypes can be retrieved from it. At this initial stage, design based upon the FBS-network is a kind of case-based reasoning”*‘*. The FBS-network makes it possible for us to apply both the prototype paradigm and the case-based paradigm to one design task.

5 Conclusions In this paper, the memory-based representation is defined as the representation for the existing form of knowledge before it is retrieved. The FBS-network is constructed as a memory-based representation for design knowledge, composed of functions, behaviours and structures from which appropriate design prototypes for particular design tasks can be retrieved. It is a function-oriented representation as well as a behaviour and a structure-oriented representation. It supports various transformations and associations between functions, behaviours and structures that may not be included in one prototype, that provide a basis for nonroutine design and creative design. Based upon the FBS-network, two kinds of design are


Figure 16

G(B)

B k 1 2 3 4 5 6 7 8 9 10 11

investigated: routine design with complete prototypes, and nonroutine design (including creative design) with incomplete prototypes. Design knowledge acquisition based on the FBS-network is also studied. It is shown that a FBS-network, as an index system of design cases, can be incrementally acquired from design cases using machine learning techniques, while the indices are in terms of function and behaviours. This implies a hopeful way of overcoming the difficulty of design knowledge acquisition.

Both the FBS-network and design prototypes are composed of functions, behaviours, structures and their relations. However, they have differ- ences. A prototype consists of knowledge generalized from a set of similar design cases, and forms a class from which individual designs can be inferred. A FBS-network consists of knowledge generalized from a set of design cases which are not necessarily alike, they are even incompatible with each other. A FBS-network does not form a class, it provides the knowledge not only for individual design situations but for a design domain consisting of various design situations. To some extent, a FBS- network can be considered as a generalized prototype for one design domain.

While at this stage of development, only partial ideas about the FBS- network can be implemented. Some mechanisms and algorithms involved in the design process based upon the FBS-network are being investigated.

6 Acknowledgments The author wishes to thank Professor Gengdong Cheng for his support of this work.


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Representing design knowledge as a network of function, behaviour and structure