Social Layers and Population Models Directed by Intelligent Agents

for Estimating the Impact of Operations and Investments

Agostino G. Bruzzone

, Marina Massei

, Christian Bartolucci

and Lorenzo Capponi D’Agostino

Simulation Team, DIME University of Genoa, via Opera Pia 15, 16145 Genova, Italy

MAST srl, via Molinero 1, 17100 Savona, Italy

Keywords: Intelligent Agents, Human Behaviour Modelling, Country Reconstruction, CIMIC, PSYOPS.

Abstract: This research aims to support operations planning and management in complex scenarios where population

and interest groups are critical elements; in particular the paper propose experimental analysis carried out on

a complex South Asia scenario by running an HLA Federation driven by Intelligent Agents; the context is

allows to simulate investments and operations over a an asymmetric mission environment with insurgents,

terrorists, different parties and articulated social frameworks. The proposed scenario is characterized by

various degrees of freedom and it needs to be modelled and simulated in order to evaluate the evolution of

human behaviour and socio-psychological aspects. The authors have developed special models in which

Computer Generated Forces (CGF) are driven by Intelligent Agents (IAs) that represents not only units on

the battlefield, but also people and interest groups (i.e. Middle Class, Nomads, Clans); the study is focused

on Civil Military Co-operations (CIMIC) and Psychological Operations (PSYOPs); while the simulation has

been developed using an architecture that involves various federates in different roles. Along the entire life

cycle of the research processes of Verification, Validation and Accreditation have been applied in order to

determine the correctness and effectiveness of the results and the paper proposes experimental results

obtained during the dynamic test of the federations.

1 INTRODUCTION

The human factors is a critical element when

investments and operations are planned over a

region; indeed the impact of population point of

view and the interests of related social layers is often

affecting effectiveness and efficiency of operations,

it could introduce risks and/or provide opportunities;

these elements are obviously pretty difficult to be

investigated therefore they strongly affect the overall

success; normally it is fundamental to identify all the

stakeholders and to consider their interests and their

attitude, this require to consider for instance

economic, religious, ethnic groups as well towns,

villages, local leaders as key factor to be consider in

planning.

In several geo-political areas it is required to plan

new investments and activities devoted to stabilize

or normalize the situation respect previous critical

conditions (i.e. civil war, insurgency, terrorism, etc);

in these case it is common to develop initiatives

devoted to get support of the local population as well

as to improve the quality of life from several point

of view (i.e. economy, health, civil rights, security,

education, etc.); in order to achieve these results

Civil Military Co-operations (CIMIC), Information

Operations (INFOPS) and Psychological Operations

(PSYOPs) need to be prepared and carried out

properly.

So it is evident the interest in being able to model

these activities as well as the dynamic interaction

with the population over a specific framework;

indeed such interactions could be pretty complex

involving many interest groups representing the

different social layers of the population as well as

their distribution over the terrain; obviously these

elements need to be considered even in reference to

the existing situation of the area from many point of

view: infrastructures (i.e. roads, hospitals, schools),

environmental conditions (i.e. weather), specific

actions (i.e. strikes, demonstrations, intimidation

activities).

The authors currently have developed models for

these context by creating a new generation of

intelligent agents representing population and

interest groups to drive complex simulation related

412

G. Bruzzone A., Massei M., Bartolucci C. and Capponi D’Agostino L..

Social Layers and Population Models Directed by Intelligent Agents for Estimating the Impact of Operations and Investments.

DOI: 10.5220/0004635704120419

In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2013),

pages 412-419

ISBN: 978-989-8565-69-3

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

to these mission environments; therefore it is

important to state that these models and the previous

considerations could be effectively tailored and

applied also in relation to civil scenarios where new

investment (i.e. industries and infrastructures) have

to be plan over a domestic region or a district as

well as during promotional campaign in marketing

initiatives.

2 SIMULATION OBJECTIVES

Therefore the international context in unstable areas

introduce a good motivation to investigate these

population models (i.e.country reconstruction

operations); currently there it is expected that these

models could be very useful for evaluating

alternative planning considering risk, opportunities,

times, resources costs over a complex and stochastic

framework, vice versa the predictive capability of

these simulators is still pretty limited due to the

high degree of uncertainty affecting human elements

and the high influence of specific spot events

(Bruzzone and Massei, 2010). Due to these

considerations, the proposed agent-driven

simulations are devoted to conduct experimental

analysis and decision support by providing reliable

estimations and useful risk analysis, but not the of

the exact time and location of a new riot; indeed

these events are generated usually by an ignition

factor that is highly unpredictable (i.e. a single

phrase or shot in a specific moment).

Considering the proposed context it is evident

that nowadays military mission environments,

especially within countries characterized by different

cultures, society organization and changeable

political situations, require a new approach to

tactical and strategic operations which not only

appreciates military engagements, but also

relationship between civil population, military forces

as well as community evolution and interest groups.

The problem of this analysis is that there are not

universally accepted simulation models and that the

human behaviour modifiers (HBM) are very difficult

to be represented; in addition it is even necessary to

create models of specific operations that are

currently not covered by the existing simulators in

order to take of Civil-Military Cooperation,

INFOPS, PSYOPS as well as of psychological

consequences over population during mission

execution; therefore some existing model/simulator

is currently taking into account these not-kinetic

operations, but usually it is just a qualitative on/off

parameters or a manual script affecting the scenario

evolution; this obviously don’t allow to consider the

complex dynamic of the interaction among different

interest groups that is the basis for situation

evolution.

3 APPLICATION FRAMEWORK

AND PROPOSED APPROACH

The simulator should consider for instance that

digging a well within an area could generate positive

effects on some part of population (i.e. people hired

to carried out the work, owner of the land) as well as

negative effects on other ones (i.e. opposite clan

respect well owner, opposite political party respect

that one involved); these actions generated direct

impact on element of the population living in the

area as well as on the their related interest group and

in addition produce a cascade effect on all the social

networks among people and interest groups. In

addition if due to weather conditions and/or lack of

resource the well constructions result to be affected

by delays this could produce negative impact on the

people that expect the completion to get benefits of

this asset.

All these elements as well as the cascade of

effects could result positive or negative with a strong

influence due to the dynamically evolving

relationships among people and interest groups and

also due to the importance of the specific actions,

the cultural background and the communications

(Seck et al., 2005).

Indeed the diffusion in the region and among the

people and interest groups of the effects of the

actions is modelled based on communications over

different supports (face to face, media, phones) and

considering specific factors; therefore these

communications introduces attenuation factors and

delays; due to the computational workload (i.e. in

our case 300’000 people and 60 interest groups) the

cascade effect could slow down simulation on single

workstations, for this reason it is possible to run the

simulation with correct diffusion models or by

considering that the diffusion happen with fixed

stochastic delays along each single operation phase

(this reduces of drastically the events to be

considered); considering multiple actions on going

concurrently and the main interest to measure final

effects this simplification resulted acceptable,

therefore if computation power is available it is

possible to run the simulator using more correct

models.

In the proposed models it was required to model

these elements and to create a simulation able to

SocialLayersandPopulationModelsDirectedbyIntelligentAgentsforEstimatingtheImpactofOperationsand

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reproduce a scenario where CIMIC and PSYOPS

were conducted; the interest group and populations

were modelled based on a multilayer approach that

consider both population elements deployed over the

terrain and interest groups; in addition these entities

interact with the units of the different parties (i.e.

coalition forces and insurgents).

Figure 1: Multilayer Model including Population, Interest

Groups and Units.

Due to the fact that the intelligent agents reproduce

population behaviour within operations, the model

allows the users to correctly evaluate the reaction of

the civilians not only to military actions, but also

during peace keeping and reconstruction phases.

The research has been developed and tested

through the involvement of Subject Matter Experts

(SME) from different countries; in particular the

proposed scenarios was developed as demonstration

for a R&D (Research and Development) project

named CIMIC and Planning Research in Complex

Operational Realistic Network (CAPRICORN).

4 INTELLIGENT AGENTS

AND SIMULATION

DEVELOPMENT

These models have been created in order to support

operational planning decisions and to be integrated

with other systems previously developed; it is able

to work as single user or within a federation

architecture (HLA standard); this was motivated not

only by the needs of respecting existing standards,

but even for the opportunity provided by developing

an open architecture to be further integrated with

other simulations in order to cover complex

problems. Due to these reason interoperability

requirements are pretty important and represent a

strategic advantage of the proposed approach

(Bruzzone et al., 2007); (Zacharewicz et al., 2008)

In fact IAs (Intelligent Agents) have a big

potential in addressing these kind of problems (i.e.

Yilmaz and

Ören,

2010).

The most important feature of the models is

represented by the intelligent agents that are able to

simulate human behavior of people modeling their

characteristics; this not only in term of their party

side (neutral forces, friends, enemies and civilians),

but even in reference to their liaisons to different

interest groups and social networks. The capability

to use this approach it is an important support for

applications involving federations of simulation to

address complex scenarios and multiple threats for

training and it is pretty interesting to investigate

their use for supporting operational planning

In the past the authors set up libraries of

innovative models able to simulate different society

attributes represented by riots, agitators and

terrorists (RATS) and IA-CGF modules (Bruzzone

et al., 2008); (Bruzzone, 2008); in some case it was

possible to simulate the whole population of a large

area reacting to a natural disasters (Bruzzone and

Massei, 2006), of a big town respect humanitarian

activities (Bruzzone et al., 2011) or in relation to

health care issues (Bruzzone et al., 2012); therefore

in this case the intelligent agents were extended to

cover, not only entities and units operating on the

field as well as single individuals/families within the

population, but even social objects such as interest

groups.

In order to succeed in this process it becomes

necessary to properly design, tailor and experiment

the scenario considering the very large quantity of

elements, variables and parameters; due to these

reasons the M&S (Modeling and Simulation)

process is formulated over three phases: simulation

development, specific mission environment tailoring

and simulation experimentation over the specific

mission environment.

The authors decided to develop an innovative

model of a whole country, taking into account the

features that involve agents able to correctly interact

in the agent based environment; obviously

considering the very broad spectrum of applications

and elements affecting these operations it is critical

to restrict the range of validity and the components

to model based on a detailed analysis to be carried

out among trans-disciplinary teams involving

scientists and operative people (Bruzzone, 2012).

Indeed considering the possibility to use these

agents in order to support decision makers on the

field for planning operations in overseas scenarios it

could be very important to develop a simulator able

to be used by people with no strong scientific

background and using limited computing capability.

Therefore it is necessary to develop models and

simulators able to run correctly based on an

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installation and configuration that should be

operated and maintained on field with the kind of

resources that are expected to be available on that

context; users should be able to configure and create

a mission environment; therefore considering the

complexity of these applications it is expected that a

team of experts and analysts will proceed to create a

configuration of the simulator for a specific region

and timeframe (i.e. Kapisa District in Afghanistan

2010) to be used as reference by final users

eventually deployed on site.

More in detail, the first step approached by the

scientists consist of Modelling phenomena, actions

and elements that are specific for a socio-political-

cultural framework. During the conceptual model

creation, simulation expert contributions are

essential to building a proper and effective set of

models and to properly approach the problem thanks

to their knowledge of specific operations and

scenarios; for instance for a region could emerge the

necessity to include nomad behaviours among the

possible alternative occupation of the population.

The second phase regards precisely the

knowledge management: info sources are used in

order to achieve the knowledge basis in order to

tailor parameters and entities of a specific region or

context; indeed to the necessity to determine a

reference scenario a specific mission environment

has to be defined; for instance it is necessary to

collect information about the different political and

economic groups as well as to tailor the importance

of religion and clan factors in term of their influence

in creating a family respect the specific cultural area.

Obviously along the entire M&S process the

VV&A has been performed to ascertain their formal

correctness and their usability effectiveness

according to the imagined use; therefore during the

simulation experimentation is the critical moment to

dynamically test the validity of the models as well as

the functionality of the simulator

A set of mission environment hypothesis are

defined by planners and/or analysts in order to

choose alternative friend course of action (F-COA)

and opposite course of action (O-COA); by this

approach it is possible to plan investments and

operations; a COA could involve CIMIC or

PSYOPS targeting different interest groups over a

specific zone, affecting people in the area as well as

social layers; in addition the decision maker could

define the operation time plan of the investment, the

assigned resource in term of money as well as

equipment and people; obviously during the

simulation multiple operations could be planned and

carried out concurrently or sequentially and the

simulator allow to consider availability of resources,

influence of opposite force actions as well as

weather condition influence (i.e. weather working

days for external constructions).

Each CIMIC or PSYOPS evolves based on

different phases (i.e. for a CIMIC planning,

engineering, acquisition of resources, erection,

commissioning) each one affected by specific needs

in term of money, resources, boundary conditions

(i.e. weather).

The agents are currently driving the behaviour of

population and interest groups respect their

perception of the general situation and their

“feelings” respect on-going activities; the models

use fuzzy rules to estimate the effect of the different

operations respect their nature and their attitude

respect the actors.

Figure 2: Fuzzy Membership for evaluating Mutual

relationships between two Groups of Interest.

Relationships among entities are usually defined

usually by two functions (impact and influence) that

could be defined in term of ownership to

membership functions respect the following classes

(low, medium, high and negative, indifferent and

positive) as proposed in the following graph.

By this approach it is possible to express

quantitative estimations about the effectiveness of

the actions conducted; for instance it is possible to

apply defuzzification in order to transform the

relationships among different critical interest groups

or people over an area in order to estimate their

trustiness respect specific players; for instance the

Overall Trustiness of the Population respect the

Coalition could be estimated or that one of a specific

religious group, village and/or district.

For each mission environment the simulation

Negative Indifferent Positive

Influence

Low Medium High

Impact

SocialLayersandPopulationModelsDirectedbyIntelligentAgentsforEstimatingtheImpactofOperationsand

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parameters have to be set according to the initial

conditions and hypothesis; then predefined settings

represent the base for the execution of the simulator,

which outputs has to be analysed.

The simulation execution could run in different

operative modes according to the context and user

requirements: stand alone, federated with other

simulators, multiple replicated runs etc.; at the end

the outputs are collected for each single run and

statistically analysed; these results are evaluated

during the simulation experimentation (Spiegel and

Schiller, 1999); a classic and simple approach for

analysing the results it is based on what if analysis

consists of the simulation of different hypothesis

previously formulated (Hill, 1996). Another

possibility that is allowed by simulator is to compare

the Desired Final Effect (DFE) of a scenario with the

Simulated Course of Action (COA).

The user can also first define Key Performance

Indexes (KPIs) and then compare final results of

different planning alternatives basing evaluation on

them; an effective approach to perform a ranking of

different alternatives consists of creating a target

function which has to be able to appreciate and

involve all these Key Performance Indexes. The

latters also represent the reference in order to

develop the cost-benefits analysis, which, together

with risk analysis, gives to the user all the elements

for choosing the best planning alternative.

Considering the complexity of the mission

environment usually Design of Experiments (DOE)

is used in order to complete analysis and produce

synthetic reports (Montgomery, 2000).

Through the feedback from military users with

operational experiences and subject matter experts

on the specific disciplines, it was possible to develop

the models as well as to define the specific user

needs; by this approach to develop and validate the

conceptual models, to perform the definition of the

specific mission environment created for

CAPRICORN Demonstrator and to validate the

functions; during the last phase related to use the

Demonstrator for testing and analysing simulation

results it was possible to complete the dynamic

VV&A of the proposed approach over a specific

case study.

In particular the involvement of the users for

VV&A was based on different phases; during the

first one the focus was to review of key concepts and

operational planning requirements; this allowed to

generate a common synthesis about CIMIC and

PSYOPS, decision making processes, scenario

analysis methodologies, training and risk analysis

requirements; most of the activities carried out in

this phase was organized mostly by desk-top review

and face validation performed through organization

of meetings and workshops; therefore in this phase

some preliminary simplified model was presented

and even executed to share concepts and to validate

and verify model assumptions and IA basic

behaviours.

In the following phases the work was based on

running of the simulation in front of the users and on

analysing experimental results: during this phase

operational planning was carried out through the

cooperation of users, analysts, subject matter

experts, development team and operational planners.

Figure 3: Simulation Interface.

5 POPULATION MODEL

For a tactic scenario, such as the real recently

warfare where Northern Atlantic countries are

involved, is necessary to model civil status and

characteristics like:

 Ethnic and Clan

 religion;

 Psychology status;

 Cultural / educational level;

 Social and Economic Status

 Geographic location of object;

 Gender;

 Age;

 Health care status;

 Political party;

And especially are considered particular modifiers

of person features, such as:

 Stress;

 Fear;

 Aggressiveness;

 Fatigue;

 Trustiness.

People relationships, friendships and social

relationships are also considered in agents

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algorithms and distributed in a stochastic manner

using Monte Carlo techniques based on consistency

algorithms able to aggregate people respect the

socio-cultural-economic background.

The layers used to model this context includes

the following classes:

Terrain Infrastructure Layers (i.e. Roads),

Terrain Elements, Weather Conditions

People The Population Entities on the

Terrain

(i.e. Mrs.Baran and/or Sakhi Family)

Groups Interest Groups (i.e. Sunnis, Honey

Producers, Hotaki Clan, Tajik Ethnic

Group, Hamnazar Political Party)

Entities Units on the Terrain (i.e. Coalition

Platoon, Insurgent Group, Riot)

The People have their social networks

interconnecting the population elements based on

familiar and friendship relationships; while each

people object is connected to multiple interest

groups based on his nature with dynamic links

representing his affiliation and the related strength;

in addition groups and interconnected by mutual

hostility and friendship over the social layer,

obviously also these connections evolve dynamically

during the simulation due the actions carried out; in

addition the terrain and Entities affect the behaviour

of people and social layers, while the Intelligent

Agents are in charge of directing the objects during

the simulation for completing tasks and for reacting

to stimuli and to their own situation awareness.

6 DEMONSTRATOR RESULTS

The Capricorn Demonstrator consists of a Simulator

including the Mission Environment Generator based

on Monte Carlo technique applied on statistical

database of the population; by this approach the

whole people objects representing population and all

related interest groups are created and

interconnected by the reference relationships over

the different layers.

It is proposed an example specific to a

CIMIC/PSYOPS mission environment in the Kapisa

Afghan region, considering the related COA and

parameters concerning population, social networks

and groups. The simulation paradigm is based on

stochastic discrete-events simulation and it is

federated within an HLA Federation (High Level

Architecture) both referring to original and

IEEE1516 standards; models were implemented in

Java with different RTI (Run Time Infrastructures)

were tested including Portico, Pitch, VT Mäk.

During the test federation integrated CAPRICORN

Simulator and IA-CGF E&U (Intelligent Agent

Computer Generated Forces Entities and Units)

developed by Simulation Team for modelling units

on the battlefield; the simulation were carried out

over Kapisa Region in South Asia considering

presence of several companies of Coalition Forces,

several units of Insurgents able to carried out O-

COA (i.e. Intimidation), Demonstrations and Riots

generated based on the population behaviours and

simulated within IA-CGF E&U; the operations (i.e.

CIMIC and PSYOPS) as well as the Interest Groups

and Population were simulated by CAPRICORN

Demonstrator over a timeframe corresponding to 1

year. ANOVA (Analysis of Variance) was applied in

order to measure the confidence band on the

controlled variables and the optimal duration time

(Mosca et al., 1993).

As anticipated the population and social

networks, within the simulation, are generated by

CAPRICORN Demonstrator basing on Monte-Carlo

techniques; Groups and people so generated relate

with entities and units as well as with

PSYOPS/CIMIC operations; the actions and events

are affected by stochastic factors considering time,

cost and effectiveness elements as well as all human

behaviour factors.

The outputs of CAPRICORN are related to

performances in term of times, costs and involved

resources during the planned operations as well as a

KPI concerning the evolution of the mutual

behaviour between the critical groups represented in

the chosen scenario (i.e. trustiness of the target

groups of the operation respect coalition forces, or

overall trustiness of the population versus coalition

forces).

Figure 4: Estimation of Trustiness among the two critical

Groups of Interest (Coalition and Target Group).

A sensitivity analysis based on DOE was carried out

respect different independent variables such as:

A: Budget Allocated to the main Operation

Effects

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

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417

B: Staff and Resources Assigned to the main

Operation

In the figure 4 the effect represents the influence

expressed as ratio between contrast and the square

pure error of trustiness scalar of the target function

“overall trustiness”; the analysis propose the effect

of single independent variables and of their

combination respect this output.

At the end of the simulation process the user

knows the effective schedule of the different

operation phases (i.e. planning, preparation,

supplying, erection/execution,

commissioning/follow-up) and the overall duration

as well as costs, cash flow, impact on the population.

The figure below shows the evolution of

trustiness during the simulation of the CIMIC action

well digging COA; it is evident that the deliverables

of the different phases introduce major changes; in

fact the simulation in this case was executed with the

simplified algorithm for diffusing of

positive/negative reinforcement due to the action

among the population the cascade.

Figure 5: Trustiness evolution with simplified model.

The model allow simulating multiple CIMIC and

PSYOPs actions within different zones and affecting

different groups; these could be planned and studied

over a single simulation run: in this case report

concerns information about their changing along the

time and their effect on the population. So for

example the solution which determines the best

impact on civilians could be identified and

quantified as well as risk estimation could be used to

support decision making.

The figure below represents the mean square

pure error diagram for a CIMIC action Digging Well

from COA in term of variance of the trustiness

respect the replicated runs carried out by changing

the random seeds of the statistical distributions; it is

evident with 25 replications it is possible to obtain

results stable with acceptable confidence in term of

trustiness (~15%).

Figure 6: Digging well action error analysis.

The model is available to be used for several

different kind of investments and infrastructures

such as school construction, police station

installation and digging wells and it could be

extended easily to irrigation infrastructures,

buildings and apartment constructions, roads; in

addition in term of INFOPS and PSYOPS it is

already possible to simulate use of Radio and TV

Media as well as leaflet campaign over a region.

In the figure below the contrast represent the

influence of factor expressed respect the target

function “Trustiness among Coalition and Sunni

Interest Group”.

7 CONCLUSIONS

This paper propose an approach to model operations

devoted to create infrastructures and actions on a

area to improve the social economic situation; the

authors developed innovative models for the

population and the interest groups devoted to

reproduce their behaviour and to estimate the impact

of the new actions the context is referring to the case

of CIMIC, INFOPS and PSYOPS conducted in

South Asia and consider both economic and

operational aspects as well as weather conditions

and possible hostile actions by insurgents.

The research proposed by the authors represents

a modelling approach for reproducing complex

behaviour among population and interest groups

during specific operations.

The experimental analysis provided interesting

results and confirmed the potential of this approach;

currently the authors are working for further extend

the current models for different applications

including industrial and civil cases over domestic

scenarios.

Trustiness COA pro Sunni

Digging Well Action

122

218

315

411

508

604

701

797

894

990

108

1183

1279

137

1472

1569

166

176

185

195

205

2147

2244

2340

243

253

2630

272

282

291

3015

311

3208

3305

Time

Trustiness

MeanSquarePureError

‐

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

0 5 10 15 20 25 30 35

RUN

MeanSquarePureError

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Figure 7: Sensitivity Analysis over Specific Trustiness.

REFERENCES

Bruzzone A. G., Massei M., (2006) “Modelling for

Estimating Impact on Road Transportation of

Regional Emergencies & Disasters” Proceedings of

HMS, Barcelona, Spain, October.

Bruzzone A. G., Massei M. (2007) "Polyfunctional

Intelligent Operational Virtual Reality Agent:

PIOVRA Final Report", EDA Technical Report

Bruzzone A. G., Scavotti A., Massei M., Tremori A.

(2008) "Metamodelling for Analyzing Scenarios of

Urban Crisis and Area Stabilization by Applying

Intelligent Agents", Proceedings of EMSS2008,

September 17-19, Campora San Giovanni (CS),Italy

Bruzzone A. G. (2008) "Intelligent Agents for Computer

Generated Forces", Invited Speech at Gesi User

Workshop, Wien, Italy, October 16-17

Bruzzone A. G., Massei M. (2010) "Intelligent Agents for

Modelling Country Reconstruction Operation",

Proceedings of AfricaMS 2010, Gaborone, Botswana,

September 6-8

Bruzzone A. G., Massei M., Tarone F., Madeo F. (2011)

"Integrating Intelligent Agents & AHP in a Complex

System Simulation", Proceedings of the international

Symposium on the AHP, Sorrento, Italy, June.

Bruzzone A. G. (2011) "Human Behavior Modeling as an

Enabling Technology for Our World Challenges",

Invited Speech I3M2011, Rome, Italy, September.

Bruzzone A. G. Tremori A., Massei M. (2011) "Adding

Smart to the Mix", Modeling Simulation & Training:

The International Defense Training Journal, 3, 25-27,

2011

Bruzzone A. G., Novak V., Madeo F. (2012) "Agent

Based Simulation Model For Obesity Epidemic

Analysis", Proceedings of I3M2012, Wien, Austria,

September

Hill David (1996) "Object-Oriented Simulation", Addison

Wesley, Reading, MA.

Mosca R, P.Giribone & A.G.Bruzzone (1993) "Optimum

Search Techniques applied to studies relative to Plant

Problems performed by means of Simulation", Proc. of

SimTec93, November , San Francisco (CA)

Montgomery D. C. (2000) "Design and Analysis of

Experiments", John Wiley & Sons, New York.

Ören, T. I. and L. Yilmaz (2009) "On the Synergy of

Simulation and Agents: An Innovation Paradigm

Perspective", Special Issue on Agent-Directed

Simulation. The International Journal of Intelligent

Control and Systems (IJICS), Vol. 14, Nb. 1, March,

4-19.

Ören, T. I. and L. Yilmaz (2012) "Agent-monitored

anticipatory multisimulation: A systems engineering

approach for threat-management Training",

Proceedings of the 24th European Modeling and

Simulation Symposium, September 19-21, Austria,

pp.277.282.

Seck, M., Frydman C., Giambiasi N., Oren TI, Ylmaz L.

(2005) "Use of a Dynamic Personality Filter in

Discrete Event Simulation of Human Behavior under

Stress and Fatigue", Foundations of Augemented

Cognition, Vol 11, pp908-917

Spiegel M. R., Schiller L.J.(1999) "Statistics", McGraw

Hill, NYC.

Yilmaz, L. and Tuncer Ören (2010) "Intelligent Agent

Technologies for Advancing Simulation-based

Systems Engineering via Agent-Directed Simulation".

SCS M&S Magazine, July.

Zacharewicz G., Frydman C., Giambiasi N. (2008) "G-

DEVS/HLA Environment for Distributed Simulations

of Workflows", Simulation, Vol.84, N.5, pp 197-213

SocialLayersandPopulationModelsDirectedbyIntelligentAgentsforEstimatingtheImpactofOperationsand

Investments

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