ontology development for agent-based collaborative design

Ontology development for agent-based collaborative design

O. O. UGWU, C. J. ANUMBA & A. THORPE

Centre for Innovative Construction Engineering, Department of Civil & Building Engineering, Loughborough University,

Loughborough LE11 3TU, UK

Abstract Domain ontologies facilitate sharing and

re-use of data and knowledge between distributed

collaborating systems. A major problem in the design

and application of intelligent systems is to capture and

understand: the data and information model that

describes the domain; the various levels of knowledge

associated with problem solving; and the patterns of

interaction, information and data ¯ow in the problem

solving space. This paper reports the development of an

ontology for agent-based collaborative design of portal

structures, using knowledge acquisition techniques and

tools. It illustrates the application of the ontology in the

development of a prototype multi-agent systems. The

study shows that a common ontology facilitates

interaction and negotiation between agents and other

distributed systems. The paper discusses the ®ndings

from the knowledge acquisition, their implications in the

design and implementation of multi-agent systems, and

gives recommendations on developing agent-based

systems for collaborative design and decision-support

in the construction sector.

Keywords collaborative design, knowledge acquisition,

multi-agent systems, ontology development, portal

frames, protocol analysis

INTRODUCTION

In a collaborative design environment, various experts

from different functional areas and design disciplines

work together to solve a given design problem. A

critical problem is to generate a common representation

of the problem-solving domain from the various

experts. Although the designers often share the same

objective such as proposing a design solution that meets

the client's requirements, they do not necessarily use

the same terminology to communicate in the design

process. Thus, even in a human-centred collaborative

design environment, communication is often hampered

because different design disciplines may use different

terms to describe the same concept, or use the same

terms to describe different concepts. This makes it

dif®cult to share information or re-use knowledge

associated with a given set of data in the domain. The

problem is exacerbated when the design process and/or

knowledge is automated and implemented in the form

of multiple intelligent agents that represent the special-

ist disciplines involved in collaborative design. One

potential solution to this communication problem is the

development of an ontology of collaborative design

concepts and terms along with their unambiguous

de®nitions. The existence of such an ontology in a

problem domain would enhance the ability of the

participating agents (both human and arti®cial) to

inter-operate without misunderstanding, and conse-

quently share or re-use data, information and know-

ledge.

Before going into a detailed discussion of the

ontology development for collaborative design, the

paper reviews related work on ontology development.

The paper describes the rationale for developing an

ontology for agent-based applications, and the meth-

odology used in the knowledge acquisition phase of the

agent-based collaborative design of light industrial

buildings (ADLIB) research project. This includes

structured interviews conducted with the domain

experts, as well as the development and evaluation of

the ontology developed from the protocol analysis. The

implications of the research ®ndings in automating

distributed collaborative design are discussed, together

with conclusions drawn from the study. The purposes

of this paper are as follows:

· To discuss the need for and importance of ontology

in collaborative design using multi-agent systems;

· To highlight methodological issues in ontology

development, from a construction perspective;

· Demonstrate its practical use in developing multi-

agent systems (MAS) applications (using the ADLIB

Prototype as an example).

Engineering, Construction and Architectural Management 2001 8 3, 211±224

211ã 2001 Blackwell Science Ltd

RELATED WORK

In recognition of the importance of ontologies in

collaborative working, some research projects have

focused on developing domain ontologies. This section

summarizes other related works. The Enterprise

Ontology is a collection of terms and de®nitions

relevant to a business enterprise. This ontology was

developed at the University of Edinburgh as part of the

Enterprise Project (Uschold & Gruninger, 1996). The

main intended uses of the ontology were to: enhance

communication between humans for the bene®t of

integration; serve as stable basis for understanding and

specifying the requirements for end-user applications

and achieve interoperability among disparate tools in an

enterprise modelling environment using suitable tool(s)

(Uschold et al., 1998). The PhysSys Ontology provides

the foundation for the conceptual database schema of

library of re-usable engineering model components.

The ontology covers different disciplines such as

mechatronics and dynamics (Borst et al., 1997). Woes-

tenek (1998) describes work on the development and

de®nition of a common vocabulary for the storage and

exchange of information in construction, undertaken as

part of the Dutch BAS project. The Process Speci®ca-

tion Language (PSL) project is devoted to the devel-

opment of taxonomy, or ontology of manufacturing

concepts and terms (URL4). The project which is

funded by the United States (US) National Institute of

Standards and Technology (NIST) systems Integration

for Manufacturing Applications (SIMA), is speci®cally

addressing manufacturing systems integration problem.

The objective is to make information interpretable

among systems and people within and across net-

worked organizations.

Other related projects focus on developing process

protocols and improving data exchange between appli-

cations. Lee et al. (1998) describes the Process Inter-

change Format (PIF) project. The project goal was to

develop an interchange format that would facilitate

automatic exchange of basic process descriptions

amongst different systems such as work¯ow systems,

¯owcharting tools, planners, and process simulation

systems. The CIMSTEEL project documented the

application protocol for the exchange of information

relating to structural steel frames (Ward & Watson,

1996). The documentation relates to computer appli-

cations that provide analysis, member design, connec-

tion design, and detailing functions for the designers

and constructors of buildings. The Process Protocol

(PP) project, which began in 1995, seeks to analyse

construction practice and create improved generic

design and construction process protocol. The project

which draws upon proven manufacturing principles, is

being developed by the University of Salford and

Loughborough University, UK, in collaboration with

a number of organizations from various disciplines

within the construction industry (URL5). Thorpe et al.

(1994) discussed the use of electronic data exchange

standards for the construction industry, to facilitate

communication between geographically distributed

organizations, and Gomez-Perez (1999) document

details of some other works in ontology development.

The above catalogue of ontology-related projects is not

exhaustive but they demonstrate the importance that

the research community attaches to ontologies in

developing intelligent and/or collaborative systems.

This paper focuses on the methodology and process

in developing an ontology for collaborative design using

multiple agents. The ontology development has been

carried out as part of the knowledge acquisition phase

of the ADLIB research project. The project is investi-

gating the use of multiple intelligent agents for the

collaborative design of light industrial buildings (Newn-

ham et al., 1999; Ugwu et al., 1999, 2000a; Anumba

et al., 2000; URL1). The main objective of the know-

ledge acquisition was to develop useful representations

of the information models of distributed design envi-

ronments, through a better understanding of the views

of different domain experts involved in a typical design

problem, and to identify the necessary design consid-

erations in developing an MAS for collaborative design.

The approaches used to achieve this objective included

using structured interviews to capture the various

domain experts' view of information as well as the

patterns of communication and interaction in the

design process. This was then used to construct an

MAS ontology that incorporates the experts' views.

ONTOLOGY

De®nitions

There has been a proliferation of de®nitions on what

exactly is meant by the term `ontology', and the

methodologies for developing an ontology. However,

the scope of this paper does not cover the epistemolo-

gical arguments on the exact meaning of ontology. In

the context of this paper two related operational

de®nitions are adopted. The most widely referenced

de®nition is that given by Gruber (1993a,b) who

de®ned ontology as `¼an explicit speci®cation of a

conceptualization'. Chandrasekaran et al. (1999)

de®ned ontologies as `¼ content theories about the

sorts of objects, properties of objects, and relation

between objects that are possible in a speci®ed domain

Ugwu, O. O. et al.212

ã 2001 Blackwell Science Ltd, Engineering, Construction and Architectural Management 8 3, 211±224

of knowledge. They provide potential terms for descri-

bing our knowledge about the domain'. The terms

Ôconcept' and Ôobject' are used interchangeably in this

paper. The next section discusses the importance of

ontology in multi-agent systems.

Importance in multi-agent systems

The MAS paradigm allows distributed organizations

and domain experts to be modelled as sets of colla-

borating (but not necessarily co-operative) agents that

interact in a problem-solving space to achieve set

goals. In the context of distributed collaborative design

problems, the goal is often to agree on a product

con®guration that is mutually satisfactory or satisfying to

the design team. The design team includes: Architect,

Structural Engineer, Steel Fabricator, Building Servi-

ces Engineer, Quantity Surveyor, Health and Safety

Personnel, and other specialist trades that are required

in a given project. However, one of the major activities

in the design and application of MAS is to capture and

understand the data and information model that

describes the domain, the various levels of knowledge

associated with problem solving, and the patterns of

communication/interaction, and information/data

¯ows in the problem solving space. The information/

knowledge is then transformed into the domain ontol-

ogy for the agents' use. The result of the interaction

analysis is often used to develop agent interaction

models within a multi-agent organization.

There are several issues that need to be addressed in

developing an MAS for collaborative design. These

issues are identi®cation of objects that de®ne the

problem domain, the roles that agents perform in the

collaborative design, the resources that these agents

require, the interaction between the agents, and the

negotiation strategy with which agents reach design

decisions. In the context of this paper, objects refer to

the various entities and classes of objects that cumula-

tively de®ne the problem domain. The objects also

include the participants from various disciplines that

are involved in the design process. Roles refer to the

various functional roles that the participants play to

realize the design objectives, and translate the design

into a physical artefact through the construction pro-

cess. In a MAS, it is particularly important to de®ne the

roles of agents so that an agent's activities are clearly

co-ordinated within the system. This will ensure that

the system does not degenerate into a state of chaos/

anarchy. However, an agent could be designed with the

capability to perform more than one role at different

times.

Resources refer to the various resources that an agent

would require to carry out its functions. In this case, it

is important to distinguish between two types of

resources ± those that would enable a given agent to

operate and participate within other community of

agents, and those that are required by the agent to carry

out its specialist functional roles. The former will be

provided by the agent platform whilst the latter would

translate to the specialist knowledge that de®ne the

agent's area of expertise. Resources can also be internal

or external. Internal resources are in-built within

(designed into) the agent whilst external resources

could be drawn from or shared with other agents. In the

real-life analogy the internal resources would be the

known facts and experiential knowledge that experts are

usually imbued with and which enables them to indulge

in intensive cognitive design process. For agent-based

systems, knowledge is modelled as sets of declarative

facts, which may be abstract or concrete concepts

(objects) in the domain. Thus, an agent's resources

could be predominantly data and information required

for design.

The external resources would refer to existing soft-

ware applications, standard codes and guidelines,

professional and institutional practice guidelines, and

organizational in-house guidelines. Inter-agent commu-

nication refers to various interactions and communica-

tion processes that would occur between the agents in

the design space. This is analogous to the inter/

intradisciplinary communication that occurs between

project team members throughout the various phases of

the project life cycle: inception, design, construction,

and demolition. We also distinguish between two types

of communication: (a) those that relate to decision-

making during speci®c design tasks and (b) those

that deal with the general issues/aspects of project

co-ordination and management such as issuing memos

that inform and keep members up-to-date with devel-

opment. Negotiation refers to the complex web of

interactions (in the form of proposals and counter

proposals) that participating agents are expected to

engage upon in order to `reach an agreement' within

the design space. However, it is possible that agents

could be unable to reach a satisfying solution, in which

case it is essential that a human user should be able to

over-ride certain decision-making criteria, or relax a

design constraint.

A major requirement in constructing ontologies for

collaborative design in the ADLIB research project, is

the need to identify and capture objects that de®ne the

domain, as well as various processes and tasks that are

involved with the design of portal frame structures.

Thus, the goals in developing the ADLIB ontology are:

Ontology development for collaborative design 213


1. To facilitate inter-agent communication using

common terminology that describes the domain of

discourse ± light industrial buildings;

2. To integrate the knowledge acquisition and model-

ling efforts for agent-based collaborative design and

improve understanding of the domain as perceived

by the participants in collaborative design;

3. To generate a set of core knowledge about colla-

borative design of portal frame structures that could

underpin further research work in agent-based and

related applications, as well as non-agent based ap-

plications.

By so doing, we seek to identify some of the generic

problem-solving methods, and map a set of appropriate

domain knowledge to the associated design tasks. It is

expected that such an ontology would facilitate data

and information processing and subsequently improve

decision making by the agents in collaborative design

environments. The methodology adopted for develop-

ing the ADLIB ontology is discussed in the next

section.

METHODOLOGY FOR DEVELOPING

THE ADLIB ONTOLOGY

As an example of how the above issues on MAS

development are addressed, the methodology used to

construct the ADLIB ontology is presented here. It

involved the following phases.

Phase 1 ± Knowledge elicitation ± domain

expert interviews

In order to elicit the various dimensions of knowledge

that are required for MAS design and development,

structured interviews were undertaken with the domain

experts (Ugwu et al., 2000a,b). The knowledge elicita-

tion process addressed several important aspects of

developing agent-based systems. These include identi-

®cation of domain ontology (Gruber, 1993a,b), de®ni-

tion of agent roles and functions, agent resource

requirements, agent interaction and negotiation as

discussed in ÔDe®nitions'. Wooldridge et al. (1999),

discussed a systematic methodology for developing

agent-based systems which comprise of analysis and

design. The analysis phase involves using powerful

abstraction mechanisms to analyse agent roles, permis-

sions, responsibilities, and interactions while the design

phase involves designing the agent model, as well as the

services and acquaintance models. The ADLIB

research project has iterated through these various

phases of MAS development, and the knowledge

acquisition process has taken into consideration the

speci®c dimensions of knowledge required in collabor-

ative design.

In order to obtain the necessary information on the

processes and data requirements in portal frame design,

nine domain experts representing different functional

disciplines in portal frame design and construction,

participated in the interviews. The participants had

several years of experience in design and project

management and represented the following design

disciplines: Architect, Structural Engineer, Building

Services Engineer, Steel Fabricator, and Health and

Safety Advisor. The other disciplines included a soft-

ware development ®rm and a steel construction

research institute. The Steel Fabricator also provided

costing information. Together these organizations pro-

vided information on constructability of designs, and

general user requirement issues in software develop-

ment.

The Architect provided a prototype case study

project and initial brief for the ADLIB project. The

case study project was used for each interview session.

The domain experts were requested to address design

problems from their own professional or disciplinary

perspective. The experts were also requested to think

aloud as far as possible whilst solving the respective

design problems and the researcher made direct obser-

vation of the processes that go into solving different

aspects of the problem. The interviews with the experts

were tape recorded, transcribed and protocol analyses

of the transcripts were carried out using an integrated

knowledge acquisition tool ± PC Pack (URL2). Refer-

ences were made to other additional sources such as

documented research papers, standard design guide-

lines, existing portal frame design software, and key-

word searches of agent-based research organizations on

the Internet to support the knowledge acquisition

process (BSI, 1990; ISO, 1994a,b; SCI, 1999; URL3).

Prior to the interview meetings, each participant was

sent a preview of the structured questions. The

researchers also requested for permission to audiotape

the interview sessions for subsequent protocol data

analysis. Having a preview of the structure of the

interview questions enabled the domain experts to

invite their colleagues into the discussion, in order

to address speci®c aspects of the questions that relate

to the colleague's design expertise in the organization.

The participants were observed in their of®ce area, and

allowed to address topics in the questions at their own

pace. Throughout the sessions, the domain experts

were asked to solve various aspects of the problem in

the case study project that relate to their functional

areas. They were also requested to make reference to



completed or ongoing projects, when necessary to

clarify their response. Such anecdotal references were

used to supplement the case studies.

The questions were phrased to capture different

characteristics of a MAS. These include ontological

requirements (domain objects, data, attributes, and

processes), agent roles and the level of functional

decomposition of tasks and activities to realize a given

role through process automation. Other MAS charac-

teristics investigated include agent negotiation strat-

egies and interaction protocols (see `De®nitions').

These questions were phrased in simple English

language terminology. Each question began with the

researcher asking the domain expert to explain his/her

role in the design of a typical portal frame structure.

The researcher then asked follow-up questions that

elicited different types of answers for encapsulation

within the MAS, and other important design issues

such as communication, negotiation and the patterns of

interaction with other design team members during a

negotiation process. These questions were designed to

prompt discussion of the different aspects of the design

process. An example question was `if you needed to

negotiate with other team members, who did you

negotiate with?' This was then followed with further

questions that explored how the negotiation was con-

ducted with respect to query generation and commu-

nication, design proposals and counter-proposals,

arguments used at each stage, concessions made, and

how agreement was ®nally reached.

When issues related to detailed decomposition of the

design processes, tasks, knowledge and data associated

with decision-taking at different decision points were

being discussed, the domain expert was requested to

demonstrate such instance of decision-making using the

case study project and any related past experience.

What emerged from these sessions were a series of

design knowledge issues including the terminology used

to describe portal structures domain, sequencing of

design tasks, information and data requirements, and

how the decomposed tasks were performed as part of

the overall design process. The sessions also captured

the descriptions of various organizational models and

structures for project design and management. This

observation has been translated into concept, and

process ontology for use in the ADLIB domain, and

is shown as Figs 1 and 2 in 'Phase 3' of the paper.

This level of face-to-face structured interviews was

particularly useful because although there exist numer-

ous references that produce high level description of

steps in the design process, such models neither

describe the pattern of reasoning and problem solving

that occur at each stage of the design process, nor do

they consider how the different experts generate alter-

native solutions to a problem. Another shortcoming of

such reference models is that it is dif®cult to capture the

¯ow of data and the inter-relationship between data at

various design interfaces. However, this level of detailed

description is essential in deployment of MAS that is

required to automate certain aspects of the design

process and hence collaborate in the problem solving

space with minimal or no intervention from the human-

user. Protocol design data of the interviews were

analysed using the methodology described in the next

session.

Phase 2 ± Protocol analysis of verbal reports

from the interviews

The transcripts of the interviews were analysed using an

integrated knowledge acquisition toolkit ± PC Pack.

The protocol analysis covered the identi®cation of

concepts, attributes that de®ne the concepts, design

processes, and facts as expressed by the domain experts.

Some of the facts were mapped into sets of meta-rules

for subsequent validation and/or more in-depth elicita-

tion with the experts.

Phase 3 ± Reformulation of the design concepts

and processes from the interviews

The viewpoints on design concepts expressed by the

domain experts were reformulated. This involved

decomposing some design concepts into more elaborate

concepts and processes. The main procedures in the

phase involved the following:

· Grouping (classifying) the experts' viewpoints into

sets of existing design concepts and processes;

· Creating new concept(s) and design process(es), and

clustering together any set of related concepts that

describe the new concept or process;

· Combining some concepts and processes that

are synonymous into a single concept (see also Phase

5).

The integrated knowledge acquisition toolkit facili-

tated the aggregation/decomposition and creation of

new concepts during the protocol analysis. Figs 1 and

2, respectively, show visual representations of the

collaborative design concepts and processes extracted

from the interviews.

As an illustration, four objects describe the `Portal

Structure' object at a high level of abstraction. These

include Foundation, Structural Frame, Architectural

layout, and `External Envelopes' which includes

cladding. However, the Structural Frame object is



an aggregation of secondary and primary elements,

and other items, whilst the primary element consists

of components such as rafter, beam, column, purlin,

and bracing. Fig. 1 also shows that the concept `cost

element' is a composition of costs related to: cold

rolled steel, fabrication, transport, erection, surface

treatment, etc., while Fig. 2 shows that the process

`design frame structure' is an aggregation of the

following tasks: get loading details, design beam

sections, design column sections, design foundation,

Figure 1 Portal frame ontology: collaborative design concepts.



Figure 2 Portal frame ontology ± design processes.



design cladding, design connections, design bolts,

etc.

Table 1 is a summary of the matrix of domain

concepts and the associated attributes at various levels

of abstraction.

Phase 4 ± Generation of relationships between

the concepts

This phase involved generating a set of relationships

that show a structural representation of the identi®ed

concepts. Fig. 3 shows a visual representation of the

relationships between the concepts.

This structural representation de®nes the interac-

tions between the various concepts in the problem

domain. In the context of MAS development, the

relationships are useful in identifying and de®ning the

roles of agents, as well as mapping design processes

and associated tasks to designated agents. Thus, it is

useful in de®ning the social behaviour of an agent

including its relationship to other agents in a MAS

environment.

Table 1 A matrix of objects and attributes at various abstraction hierarchies.

Objects

Attributes

Portal

structure Environment Beam Column Rafter Purlin Roof

Cost

element

Hot rolled

steel

Surface

treatment Fabrication

Project level

Project ID 3 3 3 3 3 3 3 3 3 3 3

Project name 3 3 3 3 3 3 3 3 3 3 3

Project description 3 3 3 3 3 3 3 3 3 3 3

Project start date 3 3 3 3 3 3 3 3 3 3 3

Project ®nish date 3 3 3 3 3 3 3 3 3 3 3

Building level (portal structure)

Building altitude 3 3 3 3 3 3

Location 3 3 3 3 3 3

Dead load 3 3 3 3 3 3

Service load 3 3 3 3 3 3

Imposed load 3 3 3 3 3 3

Effective frame

centre

3 3 3 3 3 3

Building occupancy 3 3 3 3 3 3

Building usage 3 3 3 3 3 3

Process usage 3 3 3 3 3 3

U-value 3 3 3 3 3 3

Building Height 3 3 3 3 3 3

Building Width 3 3 3 3 3 3

No. of Spans 3 3 3 3 3 3

No. of Bays 3 3 3 3 3 3

Environment

Ground condition 3

Existing utility 3

Weather 3

Boundary condition 3

Structural member level

Section size 3 3 3 3 3 3

Length 3 3 3 3 3 3

Weight 3 3 3 3 3 3

Roof structure level

Roof light 3

Roof material 3

Roof pitch 3

Costing level

Quantity 3 3 3 3

Unit rate 3 3 3 3

Cost 3 3 3 3



Phase 5 ± Veri®cation of the concepts, design

processes and rules extracted from the verbal

protocols

This phase involved veri®cation of the concepts/proces-

ses and the derivation of a common consensus on the

choice of terms. In order to carry out the veri®cation

process, the domain experts were brought together and

presented with the ontology developed from the inter-

views. The experts were also given paper copies of the

models and encouraged to give a critical analysis of the

generated models and to make amendments to the mod-

els. This exercise was particularly useful, as the experts

were able to reach an agreement on the choice of terms,

including the clustering together of synonymous terms

which different experts used to describe the same

concept or design process during the interviews. Some

design processes and attributes were added, aggregated

or decomposed as suggested by the domain experts

during the veri®cation process. The ®nal ontology

incorporates the experts' comments and suggestions.

Interestingly, during the validation session the

domain experts expressed preference for information

and data models that capture both objects and the

associated design attributes in one visual representa-

tion. The ensuing section gives a snapshot of some of

the types of information required in the various

functional areas, together with the equivalent Uni®ed

Modelling Language (UML) diagrams for the Architect

and Structural Engineer, as translated from the proto-

col analysis results (Rational, 2001):

Architectural design

Typically, the architect uses the following project

information in design: type of building and relationship

to its existing environment, cladding requirements, size

and geometry of required spaces, and roof design and

construction (Fig. 4).

Structural design

The structural engineer is interested in the following

aspects of the project information: type and use of

building, loading, existing environment and its poten-

tial impacts on geotechnical properties of the soil,

structural layout of the frame, service requirements and

loading effects, and the roof design. Fig. 5 shows a

typical detailed project information that is required for

structural design.

Steel fabrication, costing and constructability

The steel fabricator is typically interested in the

following project information: loading, joint and con-

nection loads, member sections/sizes including length

of members, and the steel grade. The aspects of the

project information that deal with costing include

quantity, unit cost of an element, and the client's

budgetary constraints. Constructability of designs

could be evaluated with respect to the following project

information: dimensions, loads, ground condition,

®nishes, and de¯ection criteria, weight and length of

member sections.

Building services design

The following aspects of the project information relate

to building services: type and use of building, space

requirements, access requirements, building occupancy

(e.g. 24 h) including type of people and light in®ltration

requirements amongst others.

Figure 3 Portal frame ontology ± relationship between concepts.



Health and safety

Safety evaluation considers the interaction of a project

with its surrounding environment. The following pro-

ject speci®c information is required: the positioning of

the building on the site in relation to any existing

building, site hazards and existing utilities. The other

environmental information includes location if there are

rivers, canals, or anything that could be a hazard. These

aggregated sets of information and data cumulatively

de®ne the `boundary conditions' of a project with

respect to safety evaluations.

IMPLEMENTATION ± THE ADLIB

APPLICATION ONTOLOGY

This section describes the implementation of some of

the concepts shown in Figs 1 and 2 at the agent

application level. It shows how the different domain

data views have been merged to generate the ontology

Project

Project ID: stringProject Name: stringProject Description: stringProject Start Date: date

Building

Job No: StringOccupancy: stringBuilding Usage: floatBuilding Height: floatBuilding Width: floatNo of People: integerLocation: stringAltitude: stringBay Centres: floatNo of Bays: integer

Design Building Frame()Check Stanchion Stability()Check Rafter Stability()

Ground condition: Enum type: hard, medium, softWeatherExisting utility services: stringStorageBoundary condition: string

Environment

Light industrial Wt Heavy Industrial Wt

External Envelope/Cladding

Cladding typeDescription

Floor

Floor thickness

Space

Distance x-axisDistance y-axis

Wall

Wall thicknessPartition wall

Brickwork

Concrete wall

Lubby Access platform

Roof

Fire wall

Eaves

Room

Administration Plant room

Fabricationroom

Show room

Toilet

Roof lighting: string

type of

Figure 4 Portal frame ontology ± architect's domain view.

ProjectProject ID: stringProject Name: stringProject Description: stringProject Start Date: date

type ofBuilding

Job No: stringOccupancy: stringBuilding Usage: floatBuilding Height: floatBuilding Width: floatNo of People: integerLocation: stringAltitude: stringBay Centres: floatNo of Bays: integer

PrimaryBuilding Frame Member

Secondary

Rafter

Frame TypeMember

Column TiesTypeSpanLocationLevel

CraneSpanLevelLhEccRhEcc

Haunch

Parapet

Building Grid

SpanHeight

Haunch LocationLengthDepthMax Cut Depth

Portal Grame DefinitionGrid LineOffsetFrame Ref

ReferenceNo Of SpanDist To Prev Frame

Frame Design Information Frame Design CombinationSelf Wt + Dead + service + ImposedFrame Dead

Building LoadDead LoadService LoadImposed LoadFrame Self Wt

Frame SpanNumberTypeSpanAxisLhEavesLhApexApexRhApexRhEaves

Frame Member RestraintMemberStatus

Enter Member()

Frame Haunch PropertiesFabricationSectionGradeStrength

Frame Member Properties

SectionGradeStrengthMpMp Reqd

Frame Loading

Load Case TitleTypeStatus

Frame Base FixityBase: stringType: stringLevel: floatVrt Stiff: floatHor Stiff: floatRot Stiff: floatCapacity Pere: floatCapacity: floatNo of Bolts: integer

Figure 5 Portal frame ontology ± Structural Engineer's domain view.



for the MAS prototype. At the application level, the

domain experts' views have been integrated with the

existing schema of a software application for portal

frame design. This software is also being integrated

with the MAS prototype in the ADLIB project. The

implemented ontology includes the following concepts

(objects) roof, and environment. Other objects include,

column, beam, rafter, and purlin, which is a subset of

the primary elements grouped under the `structural

frame' object in Fig. 1. The ontology was implemented

using BT's Zeus Agent Building Toolkit (British

Telecom, 1999) as shown in Fig. 6.

The collaborative design process has been simulated

in the ADLIB pilot demonstrator, and the agents are

able to use the ontology and carry out various design

tasks including peer-to-peer interaction, and negoti-

ation to agree on optimum designs. Each agent propo-

ses a design that maximizes its utility within the

problem design space and a ®nal design is agreed

after several rounds of negotiation. Details of this

simulation including the de®nition of roles for the task

agents, inter-agent interaction, and negotiation strat-

egies and protocols, are discussed elsewhere (Newnham

et al., 1999; Anumba et al., 2001). The agents are able

to communicate and reach some design consensus

because they share a common ontology that de®nes the

components of a portal frame structure.

DISCUSSION AND FURTHER WORK

This research revealed important factors for considera-

tion in the design of MASs for collaborative design in

the AEC sector. These are discussed below:

Process decomposition and collaborative

working practice

The research identi®ed the sequencing and decom-

position of complex tasks and activities in the design

of portal frame structures. Each design activity from a

given functional area revealed co-operative work and

a high level of interdependence of data across

different functional areas. It also demonstrated colla-

borative decision-making in such areas as selecting

member sections and length for fabrication. The

participants emphasized the need to evaluate

constructability and the health and safety implications

of design decisions at various decision points in the

design process. This is broadly in line with the

requirements of Construction, Design and Manage-

ment (CDM). These emphases suggest the need for

systems that will facilitate the processing and man-

agement of knowledge on constructability, health and

safety, and other design standards in distributed

design environments.

Figure 6 Application ontology for the ADLIB prototype.



Communications in collaborative design

The study also reveals that a complex web of interac-

tion and communication exists in the design environ-

ment, and that communication is neither linear nor

bi-directional in all instances. This has speci®c impli-

cations in the design of the interaction models for

agent-based systems. The participants representing

functional areas of design, building services, and

fabrication illustrated several identical scenarios in

evaluating certain design decisions related to construc-

tability and safety, in their respective design interfaces.

This emphasized the need for a `just-in-time' commu-

nication and collaborative decision-making.

MAS architectures

Gaines (1986, 1991) and Gaines & Shaw (1995)1 noted

that one of the major questions in applications devel-

opment remains whether the next generation of large

scale applications should be based on corporate know-

ledge repositories as extensions of databases, or on

distributed, heterogeneously integrated knowledge

bases as extensions of networked personal computing

environments. In the context of collaborative design

environments, the knowledge acquisition exercise des-

cribed in this paper has raised some issues of compu-

tational complexity, which results from the

combinatorial growth in the various domain rules that

are required for collaborative working. This suggests

that an open MAS-based infrastructure that encapsu-

lates knowledge- and rule-based systems, databases,

and computational models/algorithms, combined with

the Internet, could facilitate the level of data/system

integration, and process automation that is required in

collaborative design.

User modelling and adaptable agent±user

interfaces

The various functional roles and design activities

revealed by this study have a number of implications

for modelling MAS and the user-interfaces. The study

has helped to identify the various types of design

knowledge that must be contained in a MAS ± goal

knowledge, plan and sequencing knowledge, negoti-

ation strategies and interaction protocols. Also, the

study has facilitated identi®cation and understanding of

design attributes that are speci®c to the various func-

tional disciplines (agents), and those attributes that

need to be made globally accessible to all the agents in

the problem domain. This requires adaptable user-

interfaces that dynamically re-con®gure to suit a par-

ticular domain user based on the user's pro®le, design

activities and data requirements. This issue is being

addressed in a collaborative project between Lough-

borough and Cardiff universities.

In summary, to achieve any meaningful practical

application of MASs in a given domain, it is imper-

ative that the agents have a common global view of

the domain. This requirement for explicit domain

ontology enables the agents to interact without mis-

understanding during collaborative problem solving,

and also enhances the ability of the agents to share

knowledge and information as they collaborate to

solve design problems. This is because in such a

situation, agents act on behalf of their owners (domain

experts) to carry out the design tasks and negotiate

with other participating agents to agree on a solution

in the design space.

The experience gained from developing an ontol-

ogy for the ADLIB research project shows that the

process of building an ontology is complex and not

an exact science. This problem is more acute in

collaborative design environments wherein different

experts use different terms to express the same

concepts. The problem is also exacerbated because

the ontology development tries to capture different

objects and their attributes, as well as processes and

tasks in a collaborative design context. The work

reported in this paper has demonstrated how such

ontology development has been carried out in the

ADLIB project. Further work is needed to enlarge

the developed ontology. This would generate a more

expressive set of ontologies for activities and proces-

ses, resources, products, services, and organization for

other construction domains. This will facilitate agent

reasoning about constraint satisfaction with respect to

various design goals and organizational polices. It

will be a major step towards realizing practical

applications of MAS in solving construction domain

problems.

CONCLUSION AND RECOMMENDATION

The primary goal of this phase of the ADLIB project

was to understand the data and information that

different participants (domain experts) need to ful®l

their respective design activities, as well as the decom-

position of design tasks into discrete levels for process

automation. This was to underpin the system and user

requirements speci®cation for the MAS prototype. The

goal of capturing and developing an ontology for agent-

based collaborative design of portal frame structures

was achieved. The ensuing section brie¯y summarizes

these contributions:



· the interactions with the domain experts enabled

the researcher to gain a detailed understanding of the

planning and design processes in portal frame

structures. Moreover the information provided by

the domain experts, has provided a basis for de-

composing design task primitives into discrete ele-

ments, and has been used to generate a process

ontology for the ADLIB project;

· the knowledge acquisition process has made it

possible to observe design decision categories and

the various levels of reasoning that occur during the

design process. Each decision category de®nes

the characteristics of reasoning at that level and the

pattern of communication and interaction that

occurs between the design team at a given level.

These are very useful in the design of a MAS

organization for practical applications;

· the above have facilitated development of high level

speci®cation/functional requirements, and an

ontology for the MAS prototype. In particular, the

interviews revealed the need for adaptive and con-

®gurable user-interfaces that meet the needs of

individual users of a MAS.

This paper has discussed methodological issues in

developing an ontology for the collaborative design of

portal structures. It emphasized the important role that

explicit domain ontologies play in deploying agent-

based systems for decision-making and decision-sup-

port in various aspects of construction engineering.

These include collaborative project design, and other

potential application areas such as e-commerce trans-

actions and supply chain integration. The research

showed that although the construction industry is still

fragmented along functional lines and design disci-

plines, there are common data which need to be

effectively managed and communicated to appropriate

design team members (or their representative agents) at

various design interfaces. Thus, the need for common

domain ontologies is evident in collaborative design

environments.

The Architecture, Engineering and Construction

(AEC) sector has recognized the need for more

collaborative working using integrated project design

and management environment. This requires that the

existing specialist software in use should be able to

interact with specialist software such as CAD systems

that are used to generate 2D and 3D models, at the

appropriate design interfaces. Communication between

such heterogeneous software is complex, making the

need for explicit domain-speci®c ontologies more

evident. The development of construction-speci®c

domain ontologies will enhance the ability of design

experts to collaborate in distributed design environ-

ments. This will be a major step towards solving the

current interoperability problems in deploying hetero-

geneous software systems in the AEC sector. The

ADLIB Project is contributing in this regard by

developing an ontology for agent-based collaborative

design of portal frame structures.

ACKNOWLEDGEMENTS

This project is funded by the Engineering and Physical

Sciences Research Council (EPSRC) UK (Grant No:

GR/M42169) as part of the Innovative Manufacturing

Initiative (IMI). The project's industrial partners pro-

vided the domain knowledge used in developing the

ADLIB ontology and contributed in the veri®cation

process. They are Curtins, Wescol Glosford, WS

Atkins, HSE, Ferguson McIlveen Architects, BRE

Ltd, SCI, BT plc, and CSC Ltd.

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