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Project DANA : multi-agents simulations and fuzzy rules for
international crisis detection : can we forestall wars?
Roger F. Cozien1 , André Colautti2
French Department of Defence
Ecoles Militaires de Coëtquidan
Centre de Recherche Saint Cyr
56381 Guer Cedex
phone : +33 2 97 73 50 30
fax : + 33 2 97 73 50 83
Email : [email protected]
1
Computer & Simulation Department
2
Strategical Military Studies Department
ABSTRACT
Assessing the conflicting potential of an international situation is very important in the exercise of Defence duty. Mastering
a formal method allowing the detection of risky situations is a necessity. Our aim was to develop a highly operational
method twinned with a computer simulation tool which can explore a huge number of potential war zones, and can test
many hypotheses with high accuracy within reasonable time. We use a multi-agents system to describe an international
situation. The agent coding allows us to give computer existence to very abstract concepts such as : a government, the
economy, the armed forces, the foreign policy …We give to these agents fuzzy rules of behavior, those rules represent
human expertise. In order to yardstick our model we used the Falklands war to make our first simulations. The main
distortion between the historical reality and our simulations comes from our fuzzy controller which causes a great loss of
information. We are going to change it to a more efficient one in order to fit the historical reality. Agent coding with fuzzy
rules allows human experts to keep close to their statements and expertise, and they can handle this kind of tool quite easily.
Keywords : Agents, Fuzzy logic, Simulation, International Crisis, War
1. INTRODUCTION
"To be defeated is forgivable, to be surprised, never" - Napoléon Bonaparte
The Research Center of the French Military Officers Academy, carry out a research program in the field of International
Relations. These researches are conducted by both the Computer Department and the Strategic Military Studies Department.
Furthermore, in order to keep operational aspects to our works we have contacts with the French DOD's Strategic
Forecasting team. Everyone can easily understand that estimating the conflicting potential of any international situation is
one of the most important assets in the exercise of national defense duty. We have to master a method with high level of
effectiveness, coupled with a computer tool which is the only means enabling the exploration of numerous geographical
areas and international situations.
The importance of that asset is confirmed by the France's military policy, which assumes that in future the military forces
will mainly be committed first in multi-national operations on behalf of multi-national organizations such as the UN and
NATO, and second, as part of task forces in foreign countries as in Kosovo. We must give the military headquarters all the
information they need to understand a situation well, and we must warn the military and political authorities in advance
when something serious might happen and lead to an armed conflict. The main purpose of defense forecasting is to
dramatically increase the reactiveness of political and military authorities.
The territorial sovereignty remains the superior national interest, but, many other threats have appeared for the last 20 years.
These new threats give birth to new conflicts whose main characteristics are their varying shapes, intensity, geographical
situations, the great disparities in the strength of the warring parties, their violence, and the use of civilian populations as
both strategic and tactical means. Moreover, an economic threat appeared with the globalization of the economies. There is
no country that can undergo a major global economic crisis without trouble for its domestic economy. All those new threats
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and conflicts must be analyzed, processed and explained within the new frame of International Relations and Defense
Forecasting. We must develop technologies and means to keep a higher effectiveness to the National Defense.
Technically speaking, the project consists, in the first place, in writing a model able to describe actors defined as elementary
and autonomous decision-making units. So, the first effort is to make the list of all the actors needed, and to make this list as
exhaustive as possible, considering the situation studied. And then, in the second place, give them expertise rules of both
internal behavior and "social" behavior : the way they communicate with other actors. Before being computer agents our
actors are in fact, mathematical variables. Those variables are quite abstract notions such as "national income",
"reimbursement rate", "citizenship", …All the actors are well identified by the rules they are made of. Actually we used
fuzzy rules of behavior directly taken from human expertise. And, our actors became computer agents living their lives in a
multi-agents simulator named oRis and totally developed by F. Harrouet from the Brest National Engineering School Computer Lab. We then let the agents evolve and interact. We have a particular agent name "Conflicting Situation" which
tells us how close we may be to a war or, at least, to a serious crisis.
2. PROJECT : DANA
Thus, we use a multi-agents model with fuzzy rules to describe an international situation. At first, we decided to reduce the
"international situation" to a two-country crisis, with a third actor that can be viewed as the "rest of the world" and labeled
"UN" in our simulations. The multi-agents language we use is very close to C++ with parallel and dynamic characteristics
like Smalltalk or Java. But, one choice to be made when using a multi-agents model is the choice of granularity or the
"abstraction agents scale". In other words, which minimal meaning quantity is going to be represented in the model by one
agent. Obviously, the notion of "international situation" implies a macroscopic vision of two-country relations. We won't
pattern single individuals, but much larger concepts as : debt, national income, inflation, … which are, for instance, part of
the actor Economy. Each concept is coded in the simulations as an agent. This agent is full of fuzzy rules in the fields of
economy, sociology, foreign policy,…We areaware that our model is not a simulation of the real world but a watering-down
of the world. Our aim is not to understand every single phenomenon of the real world, but to identify macroscopic rules or
groups of rules that can lead to a war. We then decided to use 6 actors : Economy, Government, Armed Forces, Population,
Diplomacy, and the UN, and to break-up these actors into several agents. We assume that the interactions between actors is a
good abstraction of a country behavior.
As far as we are dealing with international relations and defense forecasting, the expertise rules are highly qualitative. That
is the main reason why we chose fuzzy logic to computerize the expert's knowledge. As a matter of fact it is very hard to say
"if 3 soldiers from country A are killed by 2 terrorists from country B, then the risk of war increases by 10 percent".
Actually, we hardly can say : "if soldiers from country A are killed by terrorists from country B then, possibilities of an
armed conflict increase". In our case we write rules like :"if the Reimbursement Rate is high then the Debt slightly
decreases". We coded the whole knowledge connected to the situation using 350 fuzzy rules in each country.
In order to test our model we had to choose an example of a modern conflict involving two countries, and in which we
mastered all the political and military aspects to validate the accuracy of our simulation model. We chose Great Britain and
Argentina in the early 80's. The conflicting situation stems from the fact that Argentina claimed sovereignty over the
Falkland islands. The two countries are quite different with regard to their organizations, functioning, and goals. The
economies have not the same capacities. In the same way, the diplomacies doesn't have the same strategies, and the armed
forces belong to very different types. All those aspects helped us to differentiate Argentina from GB, by only giving
different values to agents that both form Argentina and GB. Indeed, we must keep in mind that actors, agents and fuzzy
rules are the same for each country, therefore, a situation consists in two vectors, one for each country, made up of agents
values at a particular time.
One agent in our model is rather special, it is the "Conflicting Situation" agent. Each country has got one and this agent is a
combination of the level of tension and the level of claim to ownership the Falklands. This agent is a means for human
observers to know when a crisis becomes really serious and when all conditions make the situation ripe for war. Thus when
this agent bypass a threshold we are in a crisis situation and the laws that rule our model should be no more relevant and the
simulation should stop. Indeed, our purpose is to know and analyze the patterns that lead to war, but not the war itself.
We used the words "crisis", "conflict", "conflicting situation", … several times. In our researches, we defined "crisis" as the
time of rupture in an organized system. For the system this implies coming to a decision, either to stay in the state, or to
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move to an other one, with a view to return to stability. According to Raymond Aron : "When war is not thinkable, crisis is
this kind of restrained violence, made to lie heavy on your opponent's will, in order to obtain concessions that are not worth
the stake, nor the risk, of total war".
3. THE AGENTS MODEL
Our model is made up of six actors : Armed Forces, Economy, Diplomacy, Government, Population and the UN. These
actors are themselves made up of several agents. You can find the list of agents below :
Actors
ARMED FORCES
Agents
Training
Military Skill
Moral
Operational Means
Engagement Opportunities
Rate of Professional Soldiers
Recruiting and Equipment
Level
Actors
DIPLOMACY
ECONOMY
Cost of Debt
Debt
Inflation
Market
Black Market
National Income
Rate of Reimbursement
GOVERNMENT
United Nations
International Opinion
Banks
Resolution
POPULATION
Agents
Proposal Creation
Proposal Assessment
World Impact
Local Impact
World Acknowledgment
Local Acknowledgment
Conflicting Situation
Diplomatic Support
Economic Support
Military Support
Prestige Support
Type of Strategy
Persuasion
Public Opinion Pressure
International Opinion
Pressure
Opposition Unanimity
Prestige
Military Involvement Will
Political Will
Public Opinion Assent
Sense of Civic Responsibility
Standard of Living
Patriotism
Satisfaction
Fig. 1 : Actors and Agents list from our model
So, every actor is a group of several agents. These agents are linked together by fuzzy rules. Agents have been precisely
defined, and the name they were given is as evocative as possible. It is this shared semantic which makes a group of agents,
an actor. Every single agent possesses a part of the meaning associated with the actor it belongs to.
The next step consists in creating the links between agents. We are talking about direct links, which do not include any
notion of connection probability nor strength of the links. The human model designer decides whether the link exists
independently of the information sent trough it. We want to force the designer to explicitly say if the link is relevant or not.
Because, in our opinion, the danger with fuzzy logic is to digress towards permanent indecision which may remove all its
explanatory strength from the model. Later on, a link may be used to send a probability or a fuzzy value. In our model,
every link must include at least one fuzzy rule. DANA is made of 476 links for 84 agents in each country. Figure 2 shows
the simplified general behavior of DANA, and figure 3 shows details of actor "Economy", plus the links between its agents
and the other agents.
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Popul
atio
n
Economy
Government
Armed
Forces
D ipl
omac
y
Confl
icti
ng
Situation
ARGENTI
NA
UN
GREAT
BRITAI
N
Popul
atio
n
Econo
my
D ipl
omac
Gouvernment
Armed
Forces
y
Confl
icti
ng
Situat
ion
Fig. 2 : simplified two-country model
Recruiting &
Equipment
Banks
Banks
Prestige
Cost of
Debt
&
Reimbursment
Rate
Inflation
Debt
&
&
Black
Market
Sense
Repons
National
Income
of Civic
ibilities
Oper
a tional Means
Market
Economic
Suport
Political
Will
Fig. 3 : graph of the actor Economy
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Standard
of Living
4. INTRODUCING FUZZY LOGIC INTO DANA
All the terms used in the fuzzy rules have to be defined in the fuzzy controllers using the general terms of fuzzy logic, so
that the rules can be correctly interpreted by the controller. This fact restricts the number of terms and notions that may be
used. But actually, it gives the fuzzy rules quite a good generic aspect. This aspect allows us to equally say about the "debt"
agent or the "Type of Strategy" agent that they "slightly increase". In every case, it produces the agent's value increase. But,
the interpretation that should be given to each "increase" should be different. Intuitively, we do understand that the increase
in the value oh the "debt" agent means an increase in the real absolute value of the debt. But, this absolute value is not equal
to the value of the "debt" agent which is only an indicator. Concerning the "Type of Strategy" agent, we arbitrarily decided
that any of its increase would mean that the aggressiveness of the country increases.
Actors
Fuzzy Rules
Army
Diplomacy
if Civism is at least important, then Morale increases a little
if the World Impact is unacceptable, then World Acknowledgment
decreases a little
if the National Income is at least low, and if Inflation is at least
average, then the Reimbursement Rate increases
if Persuasion is at least important, then Political Will increases a lot
if Public Opinion Pressure is average, then Civism increases a little
If Conflicting Situation increases, then Resolution decreases a little
Economy
Government
Population
UNO / Rest of the World
Valuation Frequency
in Weeks
16
6
52
10
12
7
Fig. 4 : examples of fuzzy rules
The fuzzy rules have an other very important feature : the valuation frequency. This frequency is owned by each rule and
gives the simulator the number of simulation cycles between two valuations of the rule. One computer cycle represents one
week in the real world. Thus, if the rule "If the reimbursement rate is high, then the debt decreases slightly" has a 1/52 cycle
frequency, it means that this rule is valued every "real world" year. The valuation frequency is a very important data to
understand the model and the rules interactions. In figure 4, we may find a table with one rule of each actor. We can see that
the agents are linked by a fuzzy rule. Obviously, a rule belonging to a particular actor may link one of its agent to other
external agents belonging to other actors.
5. SIMULATOR AND MULTI-AGENTS
As we said previously, the Agent coding model is really suitable for this kind of simulations. When using "agent"
programming model, one important point is the means of communication between agents. In fact, (the links described
before), are not really fuzzy rules. The rule is inside the agent, it uses a numerical value as input data, then the rule
fuzzyfies, executes the fuzzy algorithm, defuzzyfies, and spreads the new value, through the link, toward its connected
agents. Precisely speaking, the links spread real numerical values, whereas fuzzy values remain in the agents.
The language oRis allows several agent means of communication : asynchronous, broadcast and reflex link. We chose the
reflex link means to spread the information through DANA's agents. Any change in the attribute α of an agent A,
automatically provokes the activation of, at least, one method of agent B, when B is connected to A by a reflex link. The
method we are talking about, is the method after(). Thus, when connecting B to A, B will be directly informed of any
change in α thanks to the activation of after(). Of course, many agents Ai can be connected to a same agent B. And, any
agent A can be connected to many agents Bi.
A simulation script is composed of several classes. We find the Vector class, whose instances are the agents. The FuzzCont
class, whose instances are the fuzzy controllers. The RL class, whose instances are the reflex links. And finally, the class
Clock gives rhythm to the simulations. The particular issue of simulation rhythm, and simulating time in general, is always
something difficult to deal with. In our case, we wanted to simulate the evolution of two artificial countries but we didn't
want to lapse into the extreme artificiality of cellular automatons. Indeed, we wanted to give the feeling of "natural"
evolution. Hopefully oRis language stands on a fully preemptive architecture. We can then assure that our simulations are
fully parallel processes. Furthermore, in each simulation cycle the agents are designated at random. We are doing it that way
on purpose so as not to introduce any priority between the agents.
6. SIMULATIONS
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The initialization of DANA is based on the historical reality. We have to put into all the agents the right numerical values
compared to the date we chose. As we chose the Falklands war to carry out our first experiments, we decided to begin the
study 2 years before the beginning of the war. So, all the agents are filled with values corresponding to the year 1980. So,
after the initialization we let DANA evolve freely according to agents interactions. Figures 5 and 6 show the first results for
the agents "Conflicting Situation" and connected agents of both countries.
Argentina
indice
16
14
12
Political Will
10
8
Military Involvement Will
6
Conflicting Sit
uation
Proposal Creation
4
Proposal Proces
sing
2
Prestige
0
0
20
40
60
80
100
Computertime
Fig. 5 : simulation results for Argentina
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120
Conflicting Situation
16
14
12
Conflicti
ng Situation
Military Involvement Will
Interest
inklands
Prestige
Politi
cal Will
Proposal
Assessment
Proposal
Creation
10
8
6
4
2
0
0
20
40
60
80
100
120
time
Fig. 6 : simulation results for Great Britain
Obviously, the Conflicting Situation of the two warring nations should have noticeably increased. But, the results of the
simulations contradict history. We first conclude that our model was false somewhere. We precisely observed the evolution
of every single agent and found some suspect behaviors. We had to know if those false evolutions were due : first to a
wrong choice of agents behavioral rules, or secondly, to maladjusted evaluation frequencies, or thirdly, to wrong or
excessive rules conclusions, or finally, to bad initialization values. By observing the agents' behaviors locally we found them
coherent. So, neither the links, nor the initialization values were to be doubted. We corrected some rules and looked for
"deviant" agents that should have provoked a chain reaction harming the simulations. However, we must keep in mind that
this kind of simulation returns information considering all the agents, and not only the Conflicting Situation. All the
behaviors must be analyzed to understand why a war may occur.
After a close look, we found that the simulator correctly and objectively simulated the code. Furthermore, the model's
behavior was perfectly explainable. This is a very positive point for computer simulation, indeed, if the simulator has a
neutral behavior, it puts the stress on the model's flaws. So, the question remains : why did we not reach the historical
reality?
Observing the simulation, we noticed that the evaluation frequency of the rules is a very important factor. Indeed, the
conclusion of a rule ending by "increase a little" is going to influence the simulation in a very different way, whether the
rule is going to be evaluated often or not. The notion of "increase a little" for one particular agent is not only define as a
percentage of increase, but is more likely define as a combination of this percentage with the value of the evaluation
frequency. But, we did not design any method to value the intensity of this combination, and whose effects seem to be quite
important.
The number of fuzzy sets also seems to be a problem. Indeed, in most cases we used 5 sets : very low, low, average, high,
very high. That's to many sets. While making the model, many ideas were transcribed by a set of fuzzy rules like :
"If the Opposition Unanimity is high, then the Engagement Opportunities decrease a little"
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"If the Opposition Unanimity is low, then the Engagement Opportunities increase a little"
The two rules above both transcribe the link between the agents "Opposition Unanimity" and "Engagement Opportunities".
Actually we don't have any other rules making the link between these agents. Thus, only a part of the fuzzy domain of the
agent "Opposition Unanimity" is processed. Indeed, only the cases where the agent is high or low are processed. This has
two consequences :
First : there is no weighting of the rules whether the "Opposition Unanimity" is more or less high. Let's assume for instance
that the "Opposition Unanimity" has a value of 6.2. In this example the "Opposition Unanimity" can be qualified as high
with a membership value at 0.9 and very high with membership value at 0.1. Thus, the "Opposition Unanimity is high"
should influence the application of the hypothesis "If the Opposition Unanimity is high" in an intense way. But, as the first
rule has not been written, the second where the "Opposition Unanimity" is very high is going to be fully applied. This
emphasizes the fact that center of area defuzzification whose principle is based on rules weighting is, in our case, defective.
This kind of defuzzification needs rules for the whole fuzzy domain.
Secondly : in our case, one may ask why the 5 rules have not been written. But it becomes more difficult when rules are
written as :
If X1 is Y1 and X2 is Y2 then Xn is Yn
With this kind of rules, if XI and X2 are defined on 5 fuzzy sets then 25 rules can be written. If Xn depends on X1, X2, X3
then we may write 75 rules, etc… But, as the whole fuzzy domain is not processed, because many rules are not written and
then can not cover all cases, we find fuzzy controllers that work only in particular cases, and sometimes never. Furthermore,
these controllers sometimes do not work with the right intensity. In our example, if the values of "Opposition Unanimity"
belong to the interval ]2 , 4[ then no rule is activated. This fact is very annoying, it means that some of the expert's ideas are
not taken into account, which obviously leads to false results.
If the simulation is so much hampered by the lack of rules' weighting it is also because of numerous defuzzifications.
Indeed, each time an agent sends a value to another, the first agent defuzzifies in order to send a physical value to the second
one. Each defuzzification made using the center of area method implies information loss. This fact added to the lack of
rules and weighting provokes some important distortions at the end of the simulations.
We spotted some other less important problems, but whose combined actions imply false results. The rules may have some
different evaluation frequencies. Actually these frequencies create a kind of priority or hierarchy between rules, which is a
good thing. Indeed, one can understand that a rule with a 1/52 frequency, (estimated once a year), has a higher hierarchical
level than a 1/4 frequency rule, (estimated every 4 weeks). Unfortunately, every time a rule is estimated, it is processed apart
from its frequency. As we don't have "conceptual" weighting we don't have temporal weighting either. So, when two rules,
with two different frequencies, have the same conclusion, like for instance "increase a little", the increase is the same
regardless of the frequency. Thus, because the increase or the decrease is the same, a high frequency rule is too often
estimated compared to low frequency rules. The last problem is due to rules density in the agents. We find some disparities
between agents : some contain many rules whereas others are quite "empty".
We corrected some problems. For example, we reduced the number of fuzzy sets to 3 in each fuzzy domain. Then we wrote
down all the rules for each controller. One of our fellow scientists, Dr Patrick Reignier from the Brest National Engineering
School - Computer Lab. - has developed a new fuzzy controller with SWI Prolog. Contrary to the first one, this controller
allows one to handle fuzzy values from any agent to any other, no matter how many agents are in-between. In other words,
one agent can send a fuzzy value to an other. There is no more need to defuzzify every time. The only defuzzification is
made at the end of the process to provide a physical value to the human observer. At the time we were writing this article,
we did not linked the new controller to oRis simulator. However, we made other simulations with the old controller but with
some problems already fixed. Figures 7 to 10 show a few results.
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G r e a t
B r i t a i n
indice
16
Conflicting Situation
14
12
Proposal Creation
10
Military Involvement Will
8
6
Political Will
4
Proposal Processing
2
Prestige
0
0
20
40
60
80
100
120
compute
r time
Fig. 7 : corrected simulations results for Great Britain
Great Britain
Involvement
Military
Will
16
14
12
10
Military
nvolvement
I
Will
Engagement Opportunities
8
Persuasion on Public Opinion
Type
Strategy
of
6
4
2
0
0
20
40
60
80
100
time
Fig. 8 : corrected simulations results for Great Britain
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120
A r g e n t i n a
16
14
12
Confl
icting
uation
Sit
10
Military Involvement
Will
Interest
8
Prest
ige
Pol
itical Will
Proposal
Assessment
6
Proposal
Creation
4
2
0
0
20
40
60
80
100
120
time
Fig. 9 : corrected simulations results for Argentina
Ag
r e innt a
16
14
12
10
Military Involvement
Will
Engageme
nt Opportuniti
es
8
Persuasi
on
Type
ofStrategy
6
4
2
0
0
20
40
60
80
100
time
Fig. 10 : corrected simulations results for Argentina
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120
As a matter of fact, the results are quite closer to the historical reality than before. Indeed, the final results still don't match
history but, locally speaking, if we consider only a few groups of agents, their behaviors are consistent with the historical
evolution.
The next step will consist in linking the new controller with oRis. However, some troubles will certainly remain, but the
fuzzy aspects of our work will be correct. So, the remaining problems may be caused by the lack of rules weighting. In other
words, our model is flat : all the rules have the same importance for the fuzzy controller; whereas they don't in the mind of
the expert. We tried several ways to give some "volume" to the model : to differentiate the rules and their respective roles in
the model. We tried to use the evaluation frequencies and we also tried to give synthetic weighting indexes to make groups
of rules which have the same relative importance in the mind of the expert. Unfortunately, this kind of methods is highly
sensitive to any variation in the indexes. Furthermore, the weighting indexes are made to give a semantic hierarchy from the
expert's point of view. And, it is very difficult to put some semantic into a model using a big set of synthetic indexes.
At that moment, we tried an another approach which consists in using the method of structural analysis. We modified this
well-known method in order to fuzzify all the processes and to allow the fuzzy weighting of fuzzy rules. The structural
analysis makes grouping of variables/agents according to their relative roles in the model. We will precisely describe this
method, its fuzzification and its benefits for our work in a second article. However, we can already say that the structural
analysis divides the variables/agents into explanatory variables, intermediary variables, output variables, and less
explanatory variables. Thanks to these groups and to the evaluation frequencies we are able to weight our fuzzy rules. If we
want to follow the logic of our work the weighting should be fuzzy too. All the details will be given in our next article.
7. CONCLUSION
Technically speaking, we tried to construct a model with many different agents, and these agents are the computer model of
very abstract concepts which require an overall view of the phenomenon we wish to computerize. Indeed, abstract concepts
may easily lead to false concepts if the human expert isn't careful about precisely define the agents. We also tried to make a
complete fuzzy agents model. To do so, every variable in DANA is a computer agent, and all these agents interact thanks to
300 fuzzy rules.
The "agent" design and coding made our work easier. In the case of DANA, using agents really helped the expert in
decomposing his knowledge into autonomous semantic entities. The phase of perfecting is also made easier with agent code.
We can check locally that the overall logic of the model is respected, and that the simulation results stand in that logic. Thus,
before looking for any historical similarity in our simulations, we try to know whether the agents have a local coherent
behavior or not. Finally, the maintenance of the computer code is much easier. Indeed, if the expert wants to change the
behavior of any agent or if he wants to delete or add one or several agents, we do not need to deal with the whole model and
code. An agent model is based on local interactions and the maintenance takes advantage from this local aspect.
Nevertheless, we discovered that a full fuzzy agents model creates many problems. We can summarize these problems in 3
main points :
First, the fuzzy controller must allow the use of fuzzy values from any agent to any other, no matter the "distance" between
them. We shouldn't need to defuzzify each time the value leaves an agent to re-enter an other because the defuzzifucation is
a loss of information and it tends to crush the variations in the agents' evolutions.
Secondly, a flat model may not restore all the nuances of the expert's thought. So, we must give volume to the model using
rules' weighting. But, this weighting must follow the logic of the model. In other words, as the expertise is mainly
qualitative, the method used to allow the expert to give weights must also be simple, clear and mainly qualitative. Thus, we
plan to use meta-fuzzy rules to weight the fuzzy rules of the first level. More precisely, we'll do a fuzzy adaptation of the
structural analysis.
Thirdly, all the rules have to be written, and we'd better use no more than 3 fuzzy sets to limit the number of rules. We must
also check that the rules are well allocated between the agents and that they are all used, if not, the thought of the expert
may be betrayed.
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Our first aim, beyond the technical challenge, is to know how we can forestall wars. We wanted to produce a fully
computerized new tool, , in the field of Strategic Forecasting. Violence is an old problem, quite as old as the first human
being. After the end of the cold war, a new kind of violence appeared. Compared to the war of past centuries, the new
conflicts are very difficult to define. They are multi-shapes. They might be due to terrorism, or nationalist claims, but also
domestic economic problems. The modern conflicts are in general violent, they last a long time, the civilian populations are
often a pressure or an exchange means. But overall, as they do not necessarily involve regular armies, but very often
nationalist or "revolutionary" armies, the modern conflicts arise very quickly anywhere in the world. Worse, several
conflicts, of various sizes, can arise at the same time.
As the new French military policy leads our armies to be engaged as task forces or peace forces under NATO or UN
command, we must accurately watch the international crises or the sources of international crises. Because each time a
geographical zone is spotted as a potential source of armed conflict it gives us time to prepare potential future military , if
not diplomatic, interventions. And it is our duty to help the military headquarters, and the armed forces in general to
anticipate crisis arising. This duty is perfectly summarized in Admiral Merlo words : "To forestall in order not to have to
fight, or to be able to extinguish the first flames very quickly, thanks to intelligence services and the right accurate
assessment of the situation".
SPIE 99 – juillet 199 - Denver, Colorado, USA
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