DESIGN OF A MODELLING LANGUAGE FOR SEMIOTICS

BASED AGENT SYSTEMS

Mangtang Chan

Department of Compute Science, City University of Hong Kong, 83 Tat Chee Avenue, Hong Kong SAR, PRC

Keywords: Semiotic agents, modelling language, semiotic framework, model-driven development.

Abstract: Semiotics has been used to describe computer programming and systems since 1960. The use of semiotics

in information system development has not yet been seen as a mainstream paradigm although a

methodology MEASUR has been established based on the semiotic framework. To evangelise the use of the

semiotic framework, this paper defines a modelling language which supports systems specification by

applying the Semantic Analysis and Norm Analysis methods in MEASUR. During the design of the

language, the Model-Drive Development approach is used as a reference and a meta-model of semiotic

agents is defined. The detail language constructs are presented and illustrated with an example of inter-agent

tracking.

1 INTRODUCTION

Discussion of relating semiotics to computers can be

dated back to as early as 1960’s when Zemanek

(1966) examines programming languages from the

viewpoint of semiotics. The three fields or

dimensions (the term used by Zemanek) of

semiotics: pragmatics, semantics and syntactics are

used to understand programming languages. A

programming language always has an interpreter,

could be human or computer that would execute the

program. The interpretation and whatever result

following would be the pragmatics, there is always

semantics about the signification of the program text

unless the programming language is not a

meaningful one, and syntactics would be the way of

how symbols or characters are combined to form the

language.

Andersen also presents semiotics as the

framework for understanding and designing

computer systems as sign systems, as targets of

interpretation (Andersen 1991). According to him, in

the total picture of a computer system, semiotic

activities could be found from the top down to the

very bottom of the system. A system could be

specified by a program text, a sign to the compiler or

interpreter which is also signs themselves. Program

execution is a process of interpretation of the

machine code by the computer processor. To the

programmer, the program text on one hand would be

transformed to assembly code and then to machine

code, on the other hand the program could also be

interpreted and new software concepts would be

created, such as statements, variables, lists, loops,

objects and modules. Repeated creation and

interpretation of programs would form a computer

system. This semiotic perspective from programs to

computer systems provided a logical extension of

the description proposed by Zemanek.

Stamper (2000) suggests a "new" direction for

systems analysis and design, and argues that existing

information systems analysis and design

methodologies since 1950 have been based on an

information flow paradigm which is data centric and

makes people think with a technical bias. He

proposes an information field paradigm - "An

information field is established by a group of people

sharing a set of norms". Norms are units of

knowledge expressed as rules based on which

subjects to whom the norms applied would act when

a certain sign occurred. He states that information

systems are organized behaviour with constant

interplay between signs (information) and norms

(knowledge). This paradigm leads to the theory of

information systems as social systems which is able

to underpin different kinds of systems with or

without the use of computers. Liu applies this

paradigm to information system engineering based

on the semiotic framework and the use of MEASUR,

Chan M.

DESIGN OF A MODELLING LANGUAGE FOR SEMIOTICS BASED AGENT SYSTEMS.

DOI: 10.5220/0003267200950102

In Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations (ICISO 2010), page

ISBN: 978-989-8425-26-3

a set of norm-oriented methods for business systems

modelling and requirement specification for

information systems (Liu 2000; Stamper 1994).

The semiotic framework has not yet been seen as

a mainstream methodology in the software industry.

Barry & Lang (2001) conducts a survey covering

multimedia and Web development techniques, and

methodologies used in companies involved in large-

scale, in-house, data-heavy business applications.

The survey concludes that practitioners are not using

models cited in the literature or research work. This

could probably be explained by the fact that use of

the models demands in-depth understanding of

relevant knowledge and the lack of tools to map the

theoretical model to implementation. On the other

hand, commercial programming languages are only

applicable to a specific problem domain or

technology platform with limited portability and

they usually work at a low level of abstraction. This

paper aims at solving this dilemma by finding a

modelling technique that is based on a sound

theoretical framework but at the same time can be

easily understood and translated into proper

implementation of different types of real-life

applications in different problem domains as well as

technology platforms.

In the past few years, the Model Driven

Development (MDD) has gained substantial

attention among practitioners with the Unified

Modelling Language (UML) (OMG. 2009) from

OMG and the Specification and Description

Language (SDL) (ITU 2000) from ITU as two

prominent examples. A model is defined as a

collection of artefacts that describes the system

(Balmelli, et al. 2006). An artefact is defined as any

item that describes the architecture, ranging from

diagram, document or specific language designed for

the description. MDD uses the technique of

abstraction to handle complex system modelling. A

system model can be looked at from different

viewpoints and abstraction levels. An abstraction or

model level is therefore a subset of the overall model

that presents a particular focus of the system.

Specification of a system with a design language is a

representation in the form of a model consisted of

different language constructs. The language

constructs are based on lower level models (meta-

models) and the specification has to be translated

into implementation which is also another kind of

model. This series of model transformations is the

essence of model-driven development. Our work is

an attempt to evangelise the use of semiotics in

information systems development and designing a

modelling language is a first step. The MDD

approach is also adopted because it is well

understood in the industry.

2 SEMIOTIC FRAMEWORK

2.1 MEASUR

The modelling language is based on the semiotic

framework and MEASUR. MEASUR as a

methodology provides five methods for information

systems development:

 Problem Articulation Methods (PAM) - to be

applied at the early stage of a project with the

aims to identify related agents and an action

course; to reveal the cultural behaviour; to

structure the problem into a main course action

and its surrounding collateral activities and to

identify norms that govern agent’s behaviour in

the system;

 Semantic Analysis Methods (SAM) - this is

basically the ontological dependency analysis to

explicate in a precise form the relationship

between words and appropriate actions used in

the system;

 Norm Analysis Methods (NAM) - this is to

specify the patterns of behaviour of the agents in

the form of norms in which responsibilities of

agents are defined, conditions in which some

actions can or cannot be performed by agents;

 Communication and Control Analysis - this is to

analyse communications between agents

identified by PAM through a classification of

messages into groups of informative,

coordinative and control according to the

intention of sender agent;

 Meta-Systems Analysis - this is to deal with the

meta-aspects of a project such as planning and

management.

Our work focuses on the modelling part and is

therefore based on the SAM and NAM.

2.2 Semiotics and the Information

Systems Development Cycle

Our aim is to use a semiotics based modelling

language to build information systems instead of

only perform the analysis and design. Liu describes

approaches of combining semiotics with other

system analysis and design techniques in going

through different stages of information systems

development with different activities. Table 1,

modified from Liu's work (Liu 2000), summarises

ICISO 2010 - International Conference on Informatics and Semiotics in Organisations

Table 1: Conceptual viewpoints in adopting semiotics for different stakeholders and IS activities.

IS activities Personnel

involved

Option 1 Option 2 Option 3

Conceptual Viewpoints

Requirement

analysis

Users, analysts Agents,

Affordances,

Norms

Agents,

Affordances,

Norms

Agents,

Affordances,

Norms

Systems

analysis

Analysts Agents,

Affordances,

Norms

Data flow diagram

(if structured

analysis

methodology is

used)

Agents,

Affordances,

Norms

Systems design Analysts,

developers

Agents,

Affordances,

Norms

Entity-

Relationships

Agents,

Affordances,

Norms

Systems

implementation

Developers Normbase Relational

database,

programming

languages, CASE

tools

Agents,

Affordances,

Norms

Systems

execution

Users,

maintenance

programmers

Normbase (as

e.g., relational

database)

Programs,

database or files

Agents,

Affordances,

Norms

the possibilities. The activities are requirement

analysis, system analysis, design, implementation

and systems execution. The conceptual viewpoints

used by different activities are compared for three

options: Option 1 described in the original work,

Option 2 to show the extreme case where totally

different methodologies and hence viewpoints are

used in different activities, and Option 3 which is the

option taken in our work. It could be noted that

Option 1 uses semiotics as the theoretical view point

throughout the activities until systems

implementation when Normbase is used. Normbase

is a software environment for managing norms and

agent semantics resulted from SAM. It is interesting

to note different types of stakeholders would use

different conceptual views in different activities in

Option 1 and 2. Because of the change in conceptual

view point, the implemented systems would not be

seen as agents functioning with the semiotic

principles. Our approach retains the use of the

semiotics view point in all activities and for all

stakeholders up to the point of systems execution.

We argue that this would offer advantages to all

stakeholders with the main one being that they can

understand each other by using a common model.

The result of this approach is that the software used

in systems execution are actually software agents

governed by norms.

2.3 Semiotic Agents

The term semiotic agent is not explicitly used in the

work of Stamper and Liu but has been found used by

Joslyn to model socio-technical organizations which

are defined as large number of groups of people

hyperlinked by information channels and interacting

with computer systems which in turn interacted with

a variety of physical systems (Joslyn & Rocha

2000). Semiotic agent is proposed by Joslyn as an

agent-based modelling technique to model and

simulate emergent decision structures in command

and control organization (e.g. 911/emergency

response systems). Design of these semiotic agents

is based on the reference and interpretation of sign

tokens. Characteristics of semiotic agent, according

to Joslyn, are:

 Capable of measuring a certain of part of the

environment and this perceived part of the

"world" gave the repertoire of behaviour and

hence its field of knowledge;

 Capable of evaluation of current status to judge

based on its own belief and determine its own

action;

DESIGN OF A MODELLING LANGUAGE FOR SEMIOTICS BASED AGENT SYSTEMS

 Able to access to a stable, decoupled memory

with which interactions with other agents can be

stored; the agent would use this memory in its

evaluation of action behaviour;

 Asynchronous in behaviour and would not

perform actions in constant time-steps with other

agents; it would respond to discrete-event clues

or follow a schedule of sequential interactions;

 Able to communicate by the creation,

transmission, receiving, storage and

interpretation of tokens based on the existence of

environmental tokens and regularities which

follow the laws of the environment and agent

rules;

 Able to share certain amount of knowledge

instead of relying on solely on individual rules or

knowledge bases.

Agents in the semiotic framework, although based

on organizational semiotics are essentially having

the same characteristics. We adopt the term semiotic

agents to refer to the software agents which are the

main building blocks in our model and systems

specified by the modelling language are therefore

multi-agent systems.

3 DEFINING THE MODELLING

LANGUAGE

3.1 The Meta Model

We name the modelling language SAME-ML

(Semiotic Agent Modelling Environment Mark-up

Language). Systems modelled by SAME-ML consist

of one or more environments which contain one or

more semiotic agents. The semiotic agents are

capable of concurrent execution. Each agent would

continuously look for signs in the environment from

its own perspective. The norms governing the

behaviour would be implemented by rule sets which

could be interpreted and checked with a rule engine.

Therefore the structure of a semiotic agent is defined

as a set of affordances implemented as functions,

methods or services depending on the implementing

technology; a set of signs, a set of rules and a rule

engine. The agent would continuously update the

sign set and assert it as facts (rule based system

terminology) to match against the rule set, the

processing is done by the rule engine which would

fire actions when certain rules are matched. Actions

are affordances of the agent. Figure 1 depicts the

abstract structure of systems produced by SAME-

ML.

Figure 1: Abstract structure of Semiotic Agents.

3.2 XML and XML Schema

We use Extensible Mark-up Language (XML) to

define SAME-ML. XML is a standard proposed by

W3C (World Wide Web Consortium) for describing

the structure of documents. The structural

information is marked by elements in the form of

tags with the format <tag> ... </tag>, known as the

open and close tags. Data are put inside the tags.

Properties known as attributes can also be specified

for the tag, such as <tag attribute="value">.

Elements can further be included within an element

to form any hierarchical structure. By looking at the

enclosing tags and according to their defined

meaning, the data can be categorized and processed.

The main difference between XML and other mark-

up languages (e.g. the HTML - Hyper Text Mark-up

Language used in Web pages) is that the meaning of

the tags is extensible (Zisman 2000). The

interpretation of the tags is not fixed but based on

another document that defines the meaning of tags.

By changing the document, different mark-up

languages can be defined for different application

domains. The definition document itself has to be

written in another language which serves as a data

definition language, schema language or meta-

language in the process of defining the mark-up

language. This definition document is in fact a

transcription of the meta-model for the language.

We choose XML Schema to specify the meta-model

of SAME-ML. XML Schema, also known as XSD

(XML Schema Definition), itself is an XML. The

whole specification of the meta-model is fairly

complex and we only use example SAME-ML to

ICISO 2010 - International Conference on Informatics and Semiotics in Organisations

show the language structure in the following

sections. More detail information about XML

Schema can be found in relevant literatures (P. V.

Biron &A. Malhotra. 2009; H. S. Thompson, D.

Beech & M. Maloney. 2009).

3.3 Detail Language Structure

3.3.1 The System

In classical semiotics, agents coexist together in an

organization interacting with each other and with the

environment. The organization can be regarded as an

information system. SAME-ML uses the highest

level element <system> to enclose all other sub-

elements. The system and the agents must be named

and the system must consist of at least one or more

agents.

......

</system>

3.3.2 Agents

In our meta-model, semiotic agents have one or

more affordances and the behaviour of each of them

is governed by a set of norms. At the level of

abstraction in SAME-ML, the details of affordance

would not be dealt with and would be left to the

implementation level. To reflect the steps in writing

the specification, a sequence is established for

defining the characteristics of agents, it is: signs,

affordances and norms. To specify an agent, there

must be a set of signs, at least one affordance and at

least one norm.

......

</agent>

3.3.3 Signs and Radical Subjectivism

One of the essential philosophical stances of the

semiotic framework, radical subjectivism, leads to

the concept of local perception of an agent where

only the signs that are of interest to the agent will be

included. The perception is then thought of as a set

of facts. The set of signs enumerates all facts that are

of interest to the agent. Because it is local, each

agent would have one and only one <signs> element

defined in it. The <fact> element can take three

possible forms: a simple definition that only

identifies the fact name and attaches a description;

the second and third form involve propagation of

fact changes to and from other agents according to

our meta-model. A fact can be changed as a result of

changes in other agents' facts (<watch>) or it could

induce changes to other agents (<watched-by>).

These two forms can be mixed together in one single

<fact> definition because of cascade propagation.

<signs>

<watch agent="X"

fact-name="AX" />

</fact>

<watched-by agent="Y"

fact-name="CY" />

</fact>

<watched-by agent="X"

fact-name="DX" />

</fact>

</signs>

In the above example, Fact A is a standalone fact.

Change of its value will not cause change to other

facts and no other fact value changes will change its

value. The fact B will have its value changed if the

value of fact AX defined in agent X is changed. In

other words, agent X is being monitored for its fact

AX. Fact C will cause change to fact CY defined in

agent Y. Finally, the value of the fact D will be

changed as a result of monitoring fact DZ and at the

same time, it is being monitored by agent X to

induce change to fact DX.

3.3.4 Norms

Format of application norms in SAME-ML is a

direct translation of classical semiotic norm.

<whenever> tag specifies the context in which this

norm is effective and the <condition> determines

whether the <action> should be taken and in what

way. The type attribute of the <deontic> is restricted

to the values of permitted, obliged and prohibited.

DESIGN OF A MODELLING LANGUAGE FOR SEMIOTICS BASED AGENT SYSTEMS

</norm>

3.3.5 Environment and Agent

Characteristics

According to the meta-model, the environment is a

conceptual entity that corresponds to an

implementation realisation of a set of software

agents being executed within a boundary. The

boundary could be a computer or a program

depending on the actual implementation. Since the

grouping of agents into an environment is an

implementation consideration, it should be separated

out from the specification of other entities to provide

easy maintenance, for example, to produce an

alternative implementation solution by regrouping

agents without changing the definitions of agents.

<contain agent=" "

think-time=" "

init=" " initi-desc=" " />

<contain agent=" "

think-time=" "

init=" "

init-desc=" " />

......

</environment>

Other attributes in the <contain> tag are used to

specify other agent run-time parameters. <think-

time> determines the responsiveness of an agent is

one of these parameters. Furthermore, there may bet

some initialization procedures to be done before an

agent is instantiated to run. <init> specifies these

procedures. Like affordance, these initialization

procedures can only be expressed as algorithmic

details and should therefore be handled at the lower

level of abstraction. It is specified as a name, similar

to affordance. The <initialization-desc> provides a

description of the initialization procedure for

documentation purpose.

4 AN EXAMPLE

4.1 Inter Agent Tracking

The following example models action tracking

commonly found in many control and monitoring

systems. Consider three agents A, B and C in an

environment where B and C would track the action

of A. A generates events b and c randomly, and B

responds to event b while C responds to c. Events b

and c are signs exhibited by A in the environment,

but from the viewpoint of B, b is the only sign of

interest and the same applies to C. This example was

implemented by first identifying the capabilities of

the agents as:

 A - firing an event at random intervals in

milliseconds (with an odd number of

milliseconds resulting in an event b, and an

even interval giving an event c);

 B - responding to event b;

 C - responding to event c.

The norms are simple:

 A - if <random interval expires> <obliged>

 B - if <event b occurs> <obliged> <B> <print

a message>

 C - if <event c occurs> <obliged> <C> <print

a message>

The specification of this example is presented in

Appendix.

4.2 Code Generation

and Implementation

A SAME-ML specification contains all information

required to produce an implementation. Scripts

developed by tools such as XSLT and XQUERY

(Chamberlin 2002; Zisman 2000) can be used to

extract information from specifications to generate

executable codes. In our implementation, we have

developed Java code templates for different types of

agents, environments and fact classes. The building

of the prototype for the above example by

transforming the specifications to implementation

codes has been done manually. In the process, code

templates for the semiotic agents, their norms and

the environment were used. The algorithmic details

of the affordances were coded as methods of the

corresponding Java agent class derived from the

templates. Information required to instantiate a

prototype is extracted from the specifications.

Norms are translated to rules according to the

specifications. Although tools for automatic code

generation have not been completed in our work,

information defined in the SAME-ML specifications

and the code templates are sufficient to support full

code generation. By changing the information

extraction scripts and the templates, codes for

different implementation platform can be produced.

ICISO 2010 - International Conference on Informatics and Semiotics in Organisations

100

5 CONCLUSIONS

To enable SAM and NAM of the semiotic

framework for wider adoption, a modelling language

for semiotics based agent systems is defined with the

MDD approach used as a reference. Semiotics and

the semiotic framework are reviewed to define a

meta-model in which semiotic agent is used as the

main component. Application of the language to an

example of inter-agent tracking shows the adequacy

in specifying agent systems. The language has also

been used in other work of multimedia systems

modelling not reported in this paper. Two key

themes are regarded as the core of MDD: raising the

level of abstraction of the models to help designers

focusing on the problem on hand instead of the

programming details and increasing the level of

automation in the transformation of the model

specification to implementation. The design of

SAME-ML fits into these two themes well although

further work has to be done to achieve full

automation in implementation generation. Further

research is being done to investigate the

incorporation of other semiotic framework concepts

such as the role of agents and their ontological

dependence into SAME-ML.

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Chamberlin, D. (2002) XQuery: An XML query language.

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DESIGN OF A MODELLING LANGUAGE FOR SEMIOTICS BASED AGENT SYSTEMS

101

APPENDIX

<system name="AgentTracking"

xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'

xmlns='http://xml.netbeans.org/schema/sameMLSchema'

xsi:schemaLocation='http://xml.netbeans.org/schema/sameMLSchema

sameMLSchema.xsd'>

<signs>

<watched-by agent="B" fact-name="Event" />

<watched-by agent="C" fact-name="Event" />

</fact>

</signs>

<description>Create event</description>

<whenever>running</whenever>

<condition>random-period-expires</condition>

</norm>

</agent>

<signs>

</fact>

</signs>

<whenever>running</whenever>

<condition>event occurs</condition>

</norm>

</agent>

<signs>

</fact>

</signs>

<whenever>running</whenever>

<condition>event occurs</condition>

</norm>

</agent>

</environment>

</system>

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