APPLYING MULTI-AGENT SYSTEMS TO ORGANIZATIONAL

MODELLING IN INDUSTRIAL ENVIRONMENTS

M. C. Romero

, R. M. Crowder

, Y. W. Sim

and T. R. Payne

Departamento de Tecnología Electrónica, University of Seville, Spain

School of Electronic and Computer Science, University of Southampton, U.K.

Keywords: Simulation, multi-agent systems, artificial social systems, engineering design, integrated product teams.

Abstract: This paper considers an agent-based approach to organizational modelling within the engineering design

domain. The interactions between individual designers within a design teams has a significant impact upon

how well a task can be performed, and hence the quality of the resultant product, hence many organisations

wish to model, and hence fully understand the process. Using multi-agent social modelling, designers and

the design task attributes can be the subject of rules implying how well tasks can be performed given

different levels of these attributes. In this paper we discuss the background to the work and the

identifications of individual, and team variables.

1 INTRODUCTION

This paper reports an approach to organisational

modelling within the engineering design domain.

While the use of software to undertake simulations

within the engineering design process, for example

computational fluid dynamics or finite element

analysis, is well known, modelling of the process

itself is less well understood. Our current research

objectives are:

 To model the organisational processes as

applied to engineering design, by bringing

together expertise in organizational practice,

agent modelling and organisational or work

psychology.

 Undertake simulations to address specific

problems within a design organisation.

In order to achieve these aims we need to

address two fundamental questions, firstly the

integration and application of a number of disparate

technologies to a demanding real world problem.

The second is extending simulation and modelling to

organisational systems, by exploiting intelligent

agent technology and the work psychologists’

understanding of the operation of individuals and

organisations. This work adopts a socio-technical

approach, combining expertise in technical and

social issues

It is widely understood that when a new design

problem emerges, the designer's knowledge related

to their previous experiences of similar problems is

applied. (Adler, Davis et al. 1989) comments that

more experienced designers are able to connect a

resolved design problem to a new problem quicker

and easier than less experienced designers. Central

to effective knowledge management is the integrated

product team or design team, where the

characteristics of the participants and their

relationships are critical. This includes their informal

network of contacts, personal experiences and the

designer’s own memory. It has been shown that

approximately 20% of the engineers time is spent

searching for and absorbing information, of which

40% will be from personal resources, even when

information is available elsewhere in the

organization (McMahon, Lowe et al. 2004)

(Shadbolt and Milton 1999).

In this work we consider the engineering design

environment, where both designers and tasks have

particular attributes. A large body of psychological

research has demonstrated that interactions between

humans and tasks have a large impact upon how

well a task can be performed. Within a computer

modelling environment, designers and task attributes

(e.g. task complexity, expertise, trust) can be the

subject of rules implying how well tasks can be

performed given different levels of these attributes

181

C. Romero M., M. Crowder R., W. Sim Y. and R. Payne T. (2008).

APPLYING MULTI-AGENT SYSTEMS TO ORGANIZATIONAL MODELLING IN INDUSTRIAL ENVIRONMENTS.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 181-186

DOI: 10.5220/0001683101810186

 SciTePress

(Martinez-Miranda, Aldea et al. 2003). For instance,

if a particular task is complex, then the designer may

need additional knowledge or expertise to complete

the task.

2 RELATED WORK

Several approaches to modelling engineering design

teams and IPTs have been reported in the literature

to date. The GRAI-Engineering approach models the

structure of the co-ordinated decision and design

activities, and is based on systems, hierarchy and

activity theory, but does not consider social

behaviour within teams (Girard and Doumeingts

2004). TEAKS (Martínez-Miranda, Aldea et al.

2006) is reported to take a multi-agent systems

approach for modelling the performance of a design

team, and hence facilitates optimization. The

variables with TEAKS are based on the PECS

(Physical condition, Emotive state, Cognitive

capabilities and Social status) reference model of

human behaviour (Schmidt 2002)

Given the characteristics of multi-Agent Systems

(Wooldridge and Jennings 1995), they can be seen

as a very useful tool for modelling human behaviour,

and in particular, social behaviour. The use of multi-

agent systems has been explored to support human

teams (Payne, Sycara et al. 2000), where agents

were used to provide support to team members given

a time-critical task, by aggregating relevant

information from their peers about other member

actions. Likewise, social dynamics have been

studied through modelling human and group

behaviour using multi-agent simulation methods

(Tsvetovat and K.Carley 2004). The agent-based

approach can enhance the potential of decentralised

computer simulation as a tool for theorizing about

social scientific issues, since it facilitates the

modelling of artificial societies of autonomous

intelligent agent.

Jennings (Jennings 2000) proposed the typical

structure of a multi-agent system (Figure 1). The

system contains a numbers of agents, which interact

with one another through communication. The

agents are able to act in an environment; different

agents have different “spheres of influence”, in the

sense that they will have control over different parts

of the environment. These spheres of influence may

coincide in some cases. The fact that these spheres

of influence may coincide may give rise to

dependency relationships between agents. When

faced with what appears to be a multi-agent domain,

it is critically important to understand the type of

interaction that takes place between the agents. In

order to clarify the interaction between

Figure 1: Canonical view of an agent-based system

(Jennings, 2000).

agents, (Jennings et al, 1998) distinguish between

cooperative models and self-interested models. In

the first type, agents cooperate to achieve a common

goal and in the second one agents negotiate in order

to achieve its own goal as best as possible.

Negotiation is seen as a method for coordination

and conflict resolution (e.g., resolving goal

disparities in planning, resolving constraints in

resource allocation, resolving task inconsistencies in

determining organizational structure). Negotiation

has also been used as a metaphor for communication

of plan changes, task allocation, or centralized

resolution of constraint violations. Hence,

negotiation is almost as ill-defined as the notion of

agent. (Jennings, Sycara et al. 1998) give what we

consider to be the main characteristics of

negotiation, which are necessary for developing

applications in the real world. These are: (a) the

presence of some form of conflict that must be

resolved in a decentralized manner, by (b) self-

interested agents, under conditions of (c) bounded

rationality, and (d) incomplete information.

Furthermore, the agents communicate and iteratively

exchange proposals and counter-proposals.

Team working processes has been extensively

studied by psychologists (Guzzo and Dickinson

1996). In a review of the research literature

(Applebaum and Blatt 1994), team working was

shown to offer organizations many advantages over

individual working and was associated with

organizational efficiency and improved quality.

However, there is widespread acceptance that

effective team-working does not result from

management; for example simply putting a group of

individuals together and expecting them to function

well as a team is rarely effective (Guzzo and

Dickinson 1996). The team's performance depends

on a variety of factors and processes concerning the

characteristics of the individual team members (e.g.

motivations, ability) and also the way the team

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interacts and works together to achieve the team's

goals (e.g. communication processes, trust, shared

understanding). As organisations continue to

recognise the benefits that teams bring to their

business, researchers are becoming increasingly in

team processes such as decision making, social

loafing, minority influence, polarization of views,

leadership, and the stages of team development.

3 DESIGN TEAM SIMULATION

When a new design problem appears, the designers’

knowledge related to previous experiences in similar

problem is readily applied. That is, the process

begins by using the designers’ knowledge of a

similar or related problem, if this approach is

possible; undertake the correct process to resolve the

new problem. Adler (Adler, Davis et al. 1989) states

that more experienced designers are able to connect

a resolved design problem to another new problem

quicker and easier. Information about how the

problems are resolved forms part of the captured

design rationale which in general, is available to

others designers who are working in related areas

(Shadbolt and Milton 1999). The process is shown in

the Figure 2.

Kno wledge

base

Design

process

Design Problem

Desig n solution

People to supply new knowledge

Kno wledge

base

Design

process

Design Problem

Desig n solution

People to supply new knowledge

Figure 2: Generic design process.

Generally, a designer follows a numbers of

steps in order to resolve the design problem. We

have to undertake detailed studies on any design

process in order to extract the rules in a design

process.

3.1 Defining the Model

We have seen also there exist a number of

technologies in industrial design sector to undertake

design projects with different complexity. Some of

those methodologies have been matured along many

years and they are defined perfectly. Although these

methodologies aren’t used to model behaviour

designer in a specific design but themselves designs,

they can help to profile how designers operate from

a general viewpoint. In order to know this behaviour

from an organisational viewpoint it’s necessary to

acquire information directly from working

designers. It is anticipated that a considerable

proportion of work will involve interviewing

designers to obtain a range of metrics.

The necessary information not only includes the

steps that designers carry out during design process,

even the interactions between different designers.

It’s in this point where social sciences come into

play. Social sciences can be applied in multi-agent

based systems modelling and simulation. (Davidsson

2002) describes a Computer Science view of agent

based social simulation (ABSS) whose intersections

are shown in Figure 3.

Social

science

gent

based

computing

Computer

simulation

SAAS

SocSim

BBS

MABS

Social

science

gent

based

computing

Computer

simulation

SAAS

SocSim

BBS

MABS

Figure 3: The intersections of the three areas defining

ABSS (Davidsson 2002).

In hierarchical model of information systems,

each information agent is responsible for providing

information about a specific domain. Information

agents further down the hierarchy provide more

specialized information about a domain. In response

to a query, an information agent may cooperate with

information agents in other domains or sub-domains,

in order to generate a response. Communication

network solutions are based on a hierarchy of

autonomous intelligent agents, which have local

decision making capabilities, but cooperate to

resolve conflicts. Higher level agents arbitrate

unresolved disputes between peer agents.

The methodology for the development of agent

societies based on this framework consists of several

levels:

• Designing coordination model.

• Defining environment in terms of global

requirements and domains

APPLYING MULTI-AGENT SYSTEMS TO ORGANIZATIONAL MODELLING IN INDUSTRIAL ENVIRONMENTS

183

• Describing behaviour in terms of agent

roles and interaction patterns.

• Defining internal structure of agents in

terms of requirements for communication,

action, interface, and reasoning behaviour.

On the other hand (Norman, Jennings et al. 1997)

propose an architecture for the business process

management that can be applied to any hierarchical

social architecture in which interaction between

different agents organized in a specific way is

necessary Figure 4)

Figure 4: Designing an agent-based business process

management system (Norman, Jennings et al. 1997).

A logical hierarchy of agencies to represent the

hierarchical interactions between human in a

business environment, consisting of:

• Agency

• Responsible agents

• Tasks

• Sub-agencies

An Agency is recursively defined: an agency

consists of a single responsible (or controlling)

agent, a –possible empty– set of tasks that the

responsible agent can perform, and a –possible

empty– set of sub-agencies (see Figure 5). The

responsible agent represents the interests of the

agency to its peers. Any communication with an

agency must go through the responsible agent. A

sub-agency typically behaves in a cooperative

manner towards its responsible agent, this agent

being responsible for representing the interests of the

agency in the wider community. This relationship

between sub-agency and responsible agent can be

viewed as a type of social commitment, and provides

a mechanism for the encapsulation and abstraction

of services.

However, in these communications, trust,

influences the relationship that exists between them.

Trust is a fundamental concern in large-scale open

distributed systems and has resulted in a

considerable amount of research and hence models

as discussed in (Huynh, Jennings et al. 2004). Trust

lies at the core of all interactions between the entities

that have to operate in such uncertain and constantly

changing environments. Trust can be defined as a

belief an agent has that the other party will do what

it says it will (being honest and reliable) or

reciprocate (being reciprocative for the common

good of both), given an opportunity to defect to get

higher payoffs.

Responsible

Agent

Responsible

Agent

Communication

and negotiation

TE1

TE2

Agency E

TD1

TD2

Agency F Agency F

Agency D

Communication

and negotiation

Agency B Agency C

Agency A

Responsible

Agent

Responsible

Agent

Communication

and negotiation

TE1

TE2

Agency E

TD1

TD2

Agency F Agency F

Agency D

Communication

and negotiation

Agency B Agency C

Agency A

Figure 5: The logical hierarchy of agencies (Norman,

Jennings et al. 1997).

The core to the Trust model is the development

of ratings upon which a decision regarding

trustworthiness is based. The ratings are normally

based on a number of metrics, for example

interaction trust, role-based trust, witness reputation,

and certified reputation. It is therefore clear that one

of the key activities within modelling the

engineering design process is to identify the

individual metrics within the concept of engineering

design, and they determine the trust rating.

3.2 Variable

A large number of variables that characterise an

engineering design environment should be

considered, including:

• The task's complexity;

• The designer's ability;

• The frequency and content of the

communication to and from designers.

• Psychological variables, including shared

understanding, trust, and motivation.

Balancing the relative importance of these variables

used within the model is fundamental to developing

a realistic simulation of design teams. Given the

difficulty of accurately quantifying such variables,

our initial approach assumes a finite, qualitative set

of descriptors (e.g. high...medium…low). The

weightings for each of the variables are adjusted on

the basis of the information collected during

interviews within actual design teams.

Agency Agency

Communication

and negotiation

Design

Business

Process

Description

Agency

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3.3 A Model of a Design Team

The design activity model used is shown in Figure 6.

The model for the individual design activity is based

on the IDED0 approach. (O'Donnell and Duffy

2002), where a number of knowledge sources are

used to provide the knowledge necessary to satisfy

the design requirements, which in this model is

defined by a goal, K

Figure 6: The design activity model.

As the teams are multi-disciplinary, it is

proposed that the task, resource, input and output

knowledge are held as vector of core competencies

also as a finite, qualitative set of descriptors. In order

for the individual activity to proceed, it operates

under a set of knowledge constraints, namely:

• K

is the internal knowledge of the designer

and can either be explicit or tacit, remain

either constant during the task.

• K

, knowledge generated as the design task

proceeds, equal K

at the completion..

• G = {G

, G

} is the constraints either

technical G

or social G

that directs and

determines the design activity.

• R, the external knowledge resources

available to the designer

Within the model a set of algorithms are used to

relate the knowledge input to the knowledge output

as a function of time. In order to simulate the

interaction between designer and a second designer

or a resources the communication paths variables are

dependant on a number of parameters including the

communication medium and context, which in term

determines the trust that the designer places on the

knowledge received. The current implementation

assumes that all requests are responded to

immediately without question.

Currently, the design activity function is linear,

but will be modified as we complete a number of

interviews with members of design teams within

manufacturing companies.

As a minimum, a team (by definition) must

consist of at least two people (with a maximum of

around 20 people), Figure 7 illustrates a two member

simplified integrated product team (IPT), working

on a single task.

Figure 7: A simplified integrated product team, showing

two designers working on a single task.

4 IMPLEMENTATION

A multi-agent system (MAS) simulation of a number

of designers has been developed using the JADE

platform. Currently, there are six designer agents,

two resource agents and a single task manager living

in the MAS, the required states and behaviours are

defined in Table 1.

Table 1: Agents’ state and behaviour.

Agents State Behaviour

Designer

Ability

Motivation

Performs task

assigned by

TaskManager

If ability is less than

task complexity, get

information from

Resource

Ability

Motivation

Ability is high

Respond with

information

TaskManager

Task progress

Task complexity

Assign tasks to agents

Keep track of task

progress

Each designer and resource agent has two

variables (ability and motivation), the values are

assigned at the start of the simulation. The task

manager agent is responsible for assigning task to

APPLYING MULTI-AGENT SYSTEMS TO ORGANIZATIONAL MODELLING IN INDUSTRIAL ENVIRONMENTS

185

the designer agents and monitors their work

progress. The design process is assumed to be in

sequential at the present stage of development phase,

i.e. designer 2 will only start work after designer 1

had completed their task (see Table 2).

Table 2: Results for a six person IPT.

Designer Ability Designer Motivation

Time to

complete

High High 18

Medium Medium 28

Low Low 52

Low Medium 36

Low High 28

Table 2 shows the results for a six person IPT

where the completion time is in arbitrary units. The

task complexity is high and the communication rules

assume that every request is handled on receipt.

5 CONCLUSIONS

This paper has reported the initial approach to the

modelling of IPTs within large engineering

organizations. Within this model we have

implemented a knowledge-time relationship which is

currently considered to be linear, but further work is

being carried out to be to optimize the algorithms

within the design activity model. The initial

feedback from our industrial partners indicates that

this is an acceptable approach for modelling.

ACKNOWLEDGEMENTS

The work described in this paper has been funded by

the DTI (Department of Trade and Industry) and

Rolls Royce under the HIPARSYS grant:

TP/3/DSM/6/I/16032. The authors acknowledge the

contribution from Mark Robinson and Helen

Jackson for the Leeds Business School (University

of Leeds) and from the Ministerio de Ciencia y

Tecnología (Spain) within I+D+I National Program

through the project with reference number

TEC2006-08430.

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