DYNAMIC REPRESENTATION OF INFORMATION FOR A

DECISION SUPPORT SYSTEM

Thierry Galinho

*,**

, Michel Coletta

, Patrick Person

, Frédéric Serin

Laboratoire Informatique du Havre, Université du Havre, Le Havre, France

Institut Supérieur d’Etudes Logistiques, Université du Havre, Le Havre, France

Keywords: MultiAgent system, Animated cartogram, Composite semantic feature, Ontology, Factual agent.

Abstract: This paper presents a system designed to help deciders manage cases of crisis. The system represents,

characterises and interprets the dynamic evolution of information describing a given situation and displays

the results of its analysis. The core of the system is made up of three multiagent systems (MAS): one MAS

for the static and dynamic representation of the information (current situation), the second MAS for

dynamically regrouping sets of agents of the former MAS and the upper MAS for matching results between

the second MAS and scenarios stored in the persistent memory of the system in order to have a deeper

analysis of the situation. The case based reasoning of this last MAS sends its results to the user as a view of

the current situation linked to some views of similar situations. In this paper, we will focus on the

representation of information MAS. This MAS is dynamic in order to be able to take into account the

changes in the description of the information. Current information is represented by a layer of factual agents

which is fed by the composite semantic features constituting the atomic data elements of information. The

aim of the set of factual agents is both to be a real snapshot of the situation at any time and to model the

evolution of information dynamically.

1 INTRODUCTION

This paper presents a global system designed to help

deciders manage cases of crisis with an original

representation of information. The system could

either be used to prevent a crisis or to deal with it. In

both cases, the main internal aim of the system is to

detect a crisis (Borodzicz & al.1993). From the

system point of view, detecting a crisis implies

representing a crisis, characterising a crisis and

comparing a crisis with other crises permanently

stored in scenarios. The result of this comparison is

provided to the user as the answer of the global

system. Our decision support system chooses to

highlight parts of scenarios close to the current

information also called situation. The information

thus obtained will help deciders analyse the current

crisis and its possible evolutions.

The core of the system which is made of three

multiagent systems (MAS) will be detailed in the

second paragraph. A common characteristic of these

three MASs is the use of intelligent agents.

Wooldridge and Jennings define these intelligent

agents (Wooldridge and Jennings 1995, 1998) which

are the only kind of agents we will consider in this

paper. Factual agents – which are our

implementation for intelligent agents for the

representation of information MAS – will also be

explained in the second and third part. The third

paragraph will focus on the design and the

implementation of the composite semantic features

and ontology, in order to measure semantic

proximities in the information representation MAS.

In the fourth part, we present some of the graphic

analysis tools we use. We will conclude our paper

with a presentation of the analysis of the choices we

made about all the parameters and strategies we had

to deal with. Some perspectives and relative works

will be considered in the last part.

Historically, the objective of the representation

of information MAS was to interpret the speech of

human actors during a crisis (Cardon 1997), (Durand

1999), (Lesage 2000). Then we applied the system

to a preventive vigil system (Boukachour & al.

2002). Its global architecture used semantic features

(SF), proximity measure, ontology, dynamic

clustering and case-based reasoning (Boukachour

2002). We wrote the software in Java for testing

purpose on some parts of real situations. Since then,

we have deeply redesigned new specifications. We

156

Galinho T., Coletta M., Person P. and Serin F. (2006).

DYNAMIC REPRESENTATION OF INFORMATION FOR A DECISION SUPPORT SYSTEM.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 156-163

DOI: 10.5220/0002491601560163

 SciTePress

implemented the prototype with an added goal:

being generic; generic is used here with a different

meaning from (Wooldridge and Jennings 1998). We

postulated that some parts of the architecture and, at

a deeper level, some parts of our agents were

independent on the subject used as application.

Today, we apply this global system to different

topics such as:

– e-learning, we started collaboration with

specialists in didactics (Bertin and Gravé 2004)

to build a “pedagogical agent” (Hubbard 2000);

– crisis management, this architecture was tested

on a scenario taken from an emergency exercise

at an oil plant in Le Havre (Boukachour & al.,

2001, 2002). The ontology of the specific

domain was created to allow comparisons

between semantic features in this context

(Boukachour & al., 2003);

– logistics and information systems (work in

progress);

– games (Person & al., 2005); the chosen game

was the game of Risk®.

Risk (Risk game, 2006) is a commercial turn-

based strategy board game produced by Parker

Brothers, a division of Hasbro. Risk shares many

characteristics with wargames, yet relative to other

war games, it is simple and abstract. It makes little

attempt to accurately simulate military strategy, the

size of the world, the logistics of long campaigns or

real-world luck. Risk is a turn-based game for two to

six players. It is played on a board depicting a

stylized political map of the Earth, divided into 42

territories, which are grouped into 6 continents.

The game is played by allocating armies to the

territories that the player controls, and then attacking

neighbouring territories in order to conquer them.

The outcome of battles is decided by rolling dice.

Some versions of the rules specify a lower

winning target or allocate a random, secret,

"mission" to each player at the beginning of the

game. Possible missions include gaining control of

all territories in two or three specified continents, or

eliminating another specified player. One of the

goals of our system is to deduce the missions of the

opponents.

Examples and figures in this paper are taken from

the game of Risk. Here are the reasons for our

choice:

– instead of depending on experts for knowing the

validity of the results, we can be experts

ourselves;

– it is easy to evaluate the quality of the advice

given by the system: we know if the system

helps us win;

– we can make the assumption of a closed-world;

– the time of execution is “reasonably” short thus

allowing the system to loop and produce enough

examples to test;

– the game of Risk is not a toy problem and it is

particularly well suited for crisis management;

– information in the game of Risk always changes

and dynamism has to be taken into account.

2 ARCHITECTURE OF THE

DECISION SUPPORT SYSTEM

The decision support system (DSS) is a tool whose

main objective is to help deciders manage decision

process in the case of a crisis or before a crisis

occurs. What this DSS offers users is to analyse the

current situation dynamically and compare it to past

situations. The past situations are permanently stored

in a scenario base and can be viewed as one part of

the knowledge we have on the specific domain.

Figure 1: Global Architecture of the DSS.

In order to be helpful for the decider, the analysis of

the current situation must be of great accuracy.

Therefore it is essential for the analysis:

– to present a synthetic view of the salient aspects

of the situation in accordance with the role and

personal interests of the given decider;

– to present possible evolutions of the current

situation with the associated consequences;

– to respect a temporal constraint according to the

time scale of the problem.

Figure 1 shows the global architecture of this

DSS. The inside query MAS and the inside

information world are in charge of all the knowledge

the core needs. The knowledge includes the scenario

base we mentioned before. The knowledge also

contains the ontology of the domain and the

proximity measure which is specific to the domain.

The outside query MAS and the outside

information systems refer to the extraction and

presentation to the core of the external information

DYNAMIC REPRESENTATION OF INFORMATION FOR A DECISION SUPPORT SYSTEM

157

the latter could need and find in network distributed

information systems. The presentation MAS will

allow dialogues between all the users authorised to

access the DSS and the core of the DSS. This MAS

also presents users with the final results of the core.

Figure 2: Architecture of the Core of the DSS.

Figure 2 shows the architecture of the core of the

DSS. The environment provides a layer between the

outer MASs presented in figure 1 and the three

MASs of the core. The three internal MASs of the

core communicate with each other and communicate

with the environment. Each MAS has one and only

one role.

The representation MAS must reflect an accurate

static view of the whole current situation and its

dynamic evolutions. The main components of this

MAS are factual agents (FA). A detailed

presentation of the architecture and the internal

structure of FAs could be found in (Person et al,

2005). The graphic analysis tools of our last part will

focus on FAs.

The characterisation MAS is an active observer

of the representative MAS. The characterisation

MAS clusters FAs both incrementally and

dynamically according to the evolution of their

internal indicators. The set of synthesis agents of the

characterisation MAS is the internal view of the

system, its internal representation of the current

situation.

The interpretation MAS takes that view, that

observation and compares the current observation

with past ones known as scenarios. The

interpretation MAS is composed of dynamic

prediction agents. A prediction agent is associated to

a given scenario or to a whole family of scenarios,

depending on the applications. Prediction agents

permanently try to match parts of their own scenario

to the view of the current situation offered by

synthesis agents. Through the environment, the

activity of prediction agents is sent to the

presentation MAS, and finally to the users.

3 INFORMATION

REPRESENTATION MAS

3.1 Environment Design

As we wrote in the introduction, to detect a crisis

implies representing a crisis and characterising it.

After this stage, we must be able to compare a crisis

with other crises. The result of this comparison is

provided to the user as the global system answer.

The observed environment is analysed and

designed as an object oriented world. That is to say

that we consider all incoming information as object

oriented messages describing states or behaviours of

objects. These objects are a viewpoint to represent

environment commonly used in object oriented

analysis and design (Barber & al, 1999). From the

object, semantic features (SF) are sent to our system.

An SF is a basic property of the environment or,

in other words, an indication that a state is changing.

In a state diagram, the state transition is used to

show the state space of a given class, the events

(messages) that cause a transition from one state to

another, and the actions that result from a state

change (Harel, 1987). Each transition occurs due to

the occurrence of an event or action from one state

to another. An event/action is directly linked above a

transition that it causes. The observed system sends

the events represented by semantic features.

We consider five parameters to identify our SF:

the object, the attribute, the value attached to this

attribute, the occurrence time of this event, and the

location (dedicated to moving objects). A SF

translates elementary information coming from the

environment both particular and partial aspects of an

observed situation. This SF design allows to obtain a

homogeneous structure. This one allows us to

establish comparisons between SFs.

3.2 Proximity Measures, Semantic

Features and Ontology

With these comparisons, the system is able to

evaluate a current situation by comparing it with

referred situations (called scenarios).

These situations of reference result from passed

experiments, studied situations, deductions, analyses

or extrapolations. We need to define the set of the

observations sent to our system, which is the goal of

the following section.

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3.3 Composite Semantic Features

In this section, we focus on the design and the

implementation of the composite semantic features.

An SF (”simple” semantic feature) is an elementary

piece of information coming from the environment.

The factual agents represent a part of information

MAS. The creation of a factual agent is triggered by

the reception of a simple SF. A new SF incoming in

the system does not always provoke the creation of a

new factual agent. A factual agent is closed to a

simple SF when the proximity measure is strictly

positive. In this case, this FA aggregates the given

SF. This aggregation is called a composite semantic

feature (CSF). The aggregation causes an update of

internal indicators.

3.4 The Game of Risk Example

The game of Risk is used to test our model. At the

beginning of our study, no type of object was

defined a priori. This study allowed us to test our

model and to define these types of objects. The

origin of the information must be treated upstream

(ontological treatment) of the creation of the

semantic feature. The different types of objects

issuing from the study can take four identified

values: territory, player, army, and continent.

Continents and territories are static objects. The

other two have dynamic properties. So for these

objects, it is necessary to associate complete

temporal data.

Figure 3: Class Diagram for the Representation of the

Game of Risk.

Continents and territories are regarded as

descriptions of a persistent situation. Continents are

sets of territories; each territory has neighbours

(other territories) and is occupied by armies owned

by a player (see figure 3). Armies and players are

activities respectively observed (occupying a

territory) and driving the actions. An action is an

attack by an army. It is an activity with a known

origin and a determined immediate goal: to conquer

a territory. We define qualifiers and their associated

values for territories. For example colour indicates

the owner (player) and force is the number of

armies.

3.5 Internal Indicators of Factual

Agents

We will now focus only on the internal indicators of

FAs. How are they defined and computed and how

could we interpret them?

An FA is the internal representation of a

composite semantic feature inside the representation

MAS. When an existing semantic feature is updated

then the corresponding factual agent will update its

internal indicators accordingly. The aim of internal

indicators of an FA is to be a synthetic

representation of the evolution of the current

situation that the characterisation MAS will deal

with. An FA has five internal indicators:

pseudoPosition (PP), pseudoSpeed (PS),

pseudoAcceleration (PA), satisfactory indicator (SI)

and constancy indicator (CI). Figure 4 shows a

partial description of the internal structure of a

factual agent.

Figure 4: Partial Internal Structure of an FA.

The proximity measure between two CSFs

returns a real number in [-1 .. 1]. This number is

then multiplied by a coefficient specific to the given

application. This result is the value of the

pseudoPosition indicator:

coefCSFCSFeasureproximityMPP

),(

211

The meaning of the pseudoPosition is to

represent the current position of an agent in the

agent representation space. We use the prefix pseudo

because we choose a constant interval of time of one

to simplify the computation of PP, PS and PA. Once

the value of PP is known, consequently PS and PA

are defined:

ttt

PPPPPS

−

++ 11

;

11 ttt

PSPSPA

−

PS evaluates the speed of the evolution of PP and

the semantic of PA is the estimation of the evolution

of PS.

DYNAMIC REPRESENTATION OF INFORMATION FOR A DECISION SUPPORT SYSTEM

159

The internal automaton of an FA is an

augmented transition network (ATN) whose

transitions are functions of PP, PS and PA. From a

generic five states ATN, each type of FA is assigned

a specific ATN. Figure 5 shows the ATN of

territories factual agents in the game of Risk. The

internal aim of a factual agent is to reach state 4 (S4)

and to stay in this particular state as long as possible.

State 1 (S1) is the initial state and states 2 (S2) and 3

(S3) are intermediary states from S1 to S4. The

transitions from a state to another state or to the

same state are determined by predicates.

Figure 5: ATN of a Territory Factual Agent.

Table 1 shows some examples of predicates in

the game of Risk.

Table 1: Example of Predicates for Transition from a

State to another State of the Internal ATN of an FA.

From To Predicate

S1 S1 PP < 1

S1 S2

PP ≥ 1

S3 S2

PS ≥ 0 and PA ≥ 0

S3 S4

PS < 0 and PA < 0

The choice of a sub ATN from a general ATN

and the definition of the predicates are specific to a

given application. But the definition of the next two

indicators is generic; these indicators must reflect

the kind of evolution of the internal ATN of an FA.

The satisfactory indicator is a valuation of the

success of an FA in reaching and staying in state 4

which is, by design, the ultimate aim of an FA.

Figure 6 presents the calculation of this indicator.

Figure 6: Calculation of Satisfactory Indicator of an FA.

The last ten transitions are summed to obtain a

value in [0 .. 20]. The higher the value, the closer to

the aim is the FA. In case of the maximal value of

20, the FA is said to be fully satisfied.

The constancy indicator will represent the

tendency of a given FA to transit both from a state to

a different state and from a state to the same state

inside the internal ATN. Figure 7 explains how this

indicator is computed.

Figure 7: Computation of Constancy Indicator of an FA.

Positive values of CI must reflect the stability

into a given state and negative values must reveal

transitions between states. Experiments led us to

choose the value of 1 for a, and the value of 9 for b

to have an indicator balanced at around 0.

4 GRAPHIC ANALYSIS TOOLS

We have created and tested some specific graphic

tools for analysing the behaviour of the

representation MAS. We plan to include parts of

these tools later in the intelligent user interface. We

will successively present a dynamic internal view of

the representation MAS, a static view of the same

MAS, a dynamic Gantt chart focusing on the

satisfactory indicator and an animated cartogram

which is a fusion of the static view of the MAS with

the pseudoPosition indicators of FAs.

A MAS could be perceived as a “black box”.

Another option is to trace the dynamic evolution of

each agent. Figure 8 displays the evolution of both

automaton and five indicators of a few FAs in the

game of Risk.

Figure 8: Partial Internal View of the Representation

MAS.

The first column is the name of territories FAs.

The other five columns are the possible states of the

internal automaton, with state 1 called “Ini” for

“initialisation”, state 2 “Delib” standing for

“deliberation”, state 3 “Deci” for “decision”, state 4

“Act” for “action” and finally “Dead” which is part

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of the generic automaton but is not used for

territories FAs. The colourisation of names indicates

when a given FA has reached a particular state in the

automaton: cyan for state 3 and magenta for state 4.

Positive values of PP, PS and PA are represented

by a coloured rectangle area in the column of the

current state of the automaton (red for PP, green for

PS and blue for PA). The last column is also used

for displaying exact values of PP, PS, PA and CI.

The coloured rectangles below the name of an FA

graphically represent CI with pink for negative

values as in “EuropeO” and red for positive values.

Each background colour in the last column

corresponds to a given interval of values of a

satisfactory indicator as specified by table 2.

Table 2: Colour of Satisfactory Indicator.

[0 .. 4] [5 .. 9] [10..14] [15..19] 20

colour

grey blue green orange red

Figure 9 displays the static view of the

representation MAS in the game of Risk. This

picture represents the board of the game with the

updated corresponding CSF used as input of the

MAS.

Figure 9: Static View of the Representation MAS.

A Gantt chart shows the timing of activities as

they occur over time. The diagram presents to our

expert the selection of factual agents whose internal

satisfactory indicator is maximal as shown in figure

10. X axis is the time and Y axis is the name of the

factual agent. We can note that only a few factual

agents are fully satisfied, for a different interval of

time and that some factual agents could be fully

satisfied a few times. We are currently designing

complementary views of this internal satisfactory

indicator.

Figure 10: Partial Gantt Diagram of Fully Satisfied FAs.

The last analysis tool is animated cartograms.

Using cartograms begun in the early days of

computer science. The basic idea is to distort a

geographical area according to a complementary

criterion you want to represent on the same map.

Tobler (Tobler, 2004) gives the following definition:

“A value-by-area cartogram is a map projection that

converts a measure of a non-negative distribution on

the earth to an area on a map.” 1960 U.S. Population

cartogram and 1981 equal population cartogram of

Britain are examples taken by Kocmoud and House

(Kocmoud & House, 1998) to compare their

algorithm with a number of existing methods. It took

about 20 hours of computer time to produce a single

cartogram. 2002 French presidential election

(Andrieu, 2002) is another example of cartogram

where the time of computation was 33 hours for a

small area. These cartograms share three common

characteristics:

– the use of static data: there is only one set of data

to work on;

– the topic in which cartograms is applied:

geography in a broad sense;

– the time to compute a cartogram.

We offer two complementary views to the users:

the static view which is the current representation of

the situation and the dynamical view with the

evolution between successive static views as

perceived by our agents. The aim of animated

cartograms is to provide users with only one

synthetic view of the situation. To do so, we face

three challenges:

– we do not have a “natural” criterion such as

density of population to compute the cartogram;

– the data are dynamic: the set of data to be used to

construct a given cartogram is permanently

updated;

– we have to compute the resulting cartogram in

quite a short span of time because we need to

provide users with the results as soon as the data

have changed.

The last two challenges could be summarised as

finding a computational method quick enough to

provide the end users with valid information. The

answer was the use of the algorithm of Gastner and

DYNAMIC REPRESENTATION OF INFORMATION FOR A DECISION SUPPORT SYSTEM

161

Newman (Gastner & Newman, 2004) who propose a

new method for constructing cartograms which is

simpler than many other methods, and therefore the

quickest to compute. They illustrated the method

with applications on the results of the 2000 U.S.

Presidential election, lung cancer cases in the State

of New York, and the geographical distribution of

stories appearing in the news. Gastner, Shalizi and

Newman (Gastner, Shalizi & Newman, 2004)

applied the same method to maps and cartograms of

the 2004 US presidential election results.

The last challenge was to find a representative

non negative distribution from internal indicators of

our agents. We called pseudoDensity the distribution

that we compute from the values of the

pseudoPosition. As this internal indicator could have

negative or positive values, we use the following

formulae to transform this indicator to a strictly

positive value:

LnitypseudoDens

max

Figure 11: Cartogram of Step 118 of the Game of Risk.

We use morphing between two successive

cartograms to alert the user that the current view will

be updated. Figure 11 shows the new shape of a

cartogram computed with pseudoDensity.

5 CONCLUSION

In this paper we describe a system designed to help

deciders interpret information of a current situation.

The system can represent information with its

dynamic evolution. The core of the system is made

of three MASs, and we have focused here on the

first layer, because it has to represent, and to store

information. The initial goal of the system was to

help deciders prevent crises by analysing the

information they have. We think that the main part

of the system is generic and can be re-used for

different applications. This is why we are testing our

system on various types of applications (prediction

crisis, game of Risk, E-learning, representation of

information). The heart and soul of the system is,

with an original representation of information and a

particular treatment of it, to be able to prevent or/and

predict (depending on the kind of application)

something will (or is) happen(ing). Representation

of information is done in the first layer we

described, by the factual agents which contain the

composite semantic features constituting the atomic

data elements of information. Some graphic tools we

use for helping the decider (but also debugging in

fact), are described in this paper. These tools help us

understand the parameters of the factual agents

which are the most accurate to characterise

information and what are the essential data to

transfer to the second layer of the global system.

We are currently working on some

complementary directions:

– developing new tools for a deeper analysis of the

MASs;

– generating a full set of scenarios for the game of

Risk. The game of Risk is an example we use to

adjust the generic aspects of the core. Other

applications will prove the genericity of the

architecture;

– connecting the representation MAS to the

characterisation MAS which is our immediate

objective.

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