=Paper=
{{Paper
|id=Vol-458/paper-9
|storemode=property
|title=Analysis and Design of a Multi-Agent System for Simulating a Crisis Response Organization
|pdfUrl=https://ceur-ws.org/Vol-458/paper3.pdf
|volume=Vol-458
}}
==Analysis and Design of a Multi-Agent System for Simulating a Crisis Response Organization==
https://ceur-ws.org/Vol-458/paper3.pdf
Analysis and Design of a Multi-Agent System for
Simulating a Crisis Response Organization
Rafael A. Gonzalez
Delft University of Technology, Jaffalaan 5,
2628BX Delft, The Netherlands
r.a.gonzalez@tudelft.nl
Abstract. Simulation is a way to deal with the lack of data and difficulty in
designing controlled experiments in the field of crisis response. This paper
presents the analysis and design of a simulation model used to evaluate
different coordination mechanisms for a crisis response organization. Such
organizations are often multidisciplinary, short-lived and ad hoc. Coordination
between the responders can be achieved in a structured way (through standards
and hierarchy) or can manifest itself in an adaptive or emergent manner. The
characteristics of the response organization and the study of structured vs.
emergent coordination fit with the capabilities and nature of multi-agent
systems (MAS). The MAS model is built using the GAIA methodology and the
JADE agent framework. The model can be configured differently to deal with
an emergency scenario developed separately as a discrete-event simulation,
providing a testbed for simulating coordination in crisis response.
Keywords: Multi-Agent Systems, Simulation, Coordination, Crisis Response.
1 Introduction
Simulation provides a unique way of understanding complex social phenomena and
crisis response organizations in particular [1]. It can be used when the cost of
collecting data is prohibitively expensive or there are a large number of conditions to
test, as is often the case in crisis response. In situations where large numbers of
responders (fire, police, medical, and other agencies) are involved, it is unfeasible to
carry out experiments in real-life situations; therefore, simulations offer a valuable
platform for testing strategies in advance [2]. Simulation can be used to provide a
more economical method of testing contingency plans and practicing coordination
between different agencies during crisis response operations [1]. Simulations can
illustrate the patterns and pathologies of crisis decision making; they can create a
great opportunity for getting acquainted with all aspects of crisis management; and
they can help bridge the gap between theory and practice [3]. Simulation is also
convenient because it offers a large degree of control for analysts and researchers [1].
This paper presents the analysis and design of a multi-agent system (MAS) to
represent a crisis response organization for simulation. The research question that
motivates the simulation project is: How do structured and emergent coordination
Proceedings of EOMAS 2009
mechanisms between crisis responders perform against each other in terms of
effectiveness and efficiency and what are the conditions under which emergent
coordination mechanisms perform better? The simulation approach is both agent-
based and discrete-event based. The MAS represents the crisis response organization,
which is the subject of the experiments. A discrete-event environment is built
alongside to simulate the crisis scenario to which the organization must respond to.
The focus of this paper is on the analysis and design of the MAS, while the
development of the discrete-event crisis scenario is outside its scope. However, it
should be noted that the idea of developing the two dimensions separately is the
ability to modify the response organization independently of the crisis scenario used.
Conversely, the same MAS organization can be tested with different scenarios built as
separate discrete-event simulations. For details on this, see [4].
Before the analysis and design it is worth discussing why a MAS is an appropriate
representation of a crisis response organization. When a crisis or emergency occurs it
gives rise to an incident organization, which is a temporary organization of otherwise
disparate resources drawn from many agencies [5]. Within this incident organization
lies a disaster management system comprising the people, technology and procedures
concerned with directing resources [5]. Participants in this disaster management
system may not have worked together before. Moreover, large-scale emergencies are
often beyond the capabilities of the permanent staff and facilities available [6]. The
resulting ad hoc crisis response teams must be formed quickly, assigned roles and
responsibilities, and deployed. The teams are not fixed, but evolve as the availability
of personnel, including volunteers, fluctuates. The corresponding entrance and exit of
teams increases the difficulty of coordinating the response. As response operations
evolve, interactions also need to be redefined for each succeeding situation [7].
Accordingly, a response to an extreme event requires organizational
interoperability through a common structure and process, along with the absorption of
volunteers and emergent organizations [8]. As a consequence, a crisis requires the
reworking of established and standardized procedures through a combination of
certain aspects of emergent behaviour and routinized organizational behaviour [9].
Thus, crisis response organizations must be open systems that promote distributed
decision making and improvisation in the face of unexpected events or conditions [8].
There is the belief that because the military command and control system is
effective in deploying resources, it must be capable of effectively and efficiently
providing rescue and relief services, but the military is not trained or structured for the
complex tasks of intergovernmental coordination and collaboration needed when
preparing for and responding to extreme events [8]. In addition, while hierarchical
networks work efficiently during routine operations, they do so poorly in the dynamic
environment of emergencies, where node failure may isolate large networks from
each other [10]. This has resulted in the tendency towards designing emergent and
dynamic networks, rather than formal, static and hierarchical organizations [11, 12].
In practice, most crisis response organizations exhibit some degree of autonomy,
while preserving centralization for coordination [8, 11].
In brief, crisis response organizations are fluctuating in size, formed in an ad hoc
way and multidisciplinary; at the same time, they exhibit hierarchy and centralization
together with emergence, autonomy, openness and scalability. If we define an
organization as an open system consisting of cognitively restricted, socially situated,
Proceedings of EOMAS 2009
and task-oriented actors who interact with other members of the organization and are
affected by ambiguity and past experience, then computational models can be used to
encapsulate this view and generate predictions regarding the design of an organization
for effective performance in response to a crisis [13]. An adequate computational
model, given the characteristics of crisis response organizations is a multi-agent
system (MAS). Such a system may exhibit similar behaviour, such as a distributed
organizational framework, mobility and self-coordination [12, 14].
As a result, MAS are used frequently in crisis response related research. When
agents are thought of as functional software units with the capability to execute pre-
defined tasks autonomously, they can support the decision-making process of human
responders [15]. Agents can extract knowledge from the Internet and inform affected
communities and relevant authorities [6, 16]. For example, they can support the
decision-making process during the medical response to a large incident, by
monitoring news feeds and unloading decision-makers of part of their information-
processing needs [17] or by supporting the decisions regarding the distribution of
patients in accordance with the availability of resources [18]. They can also be used
for fusing the heterogeneous information that they themselves extract [19]. Lastly,
decision-support may involve reasoning about mission structures, resource
limitations, time considerations, and interactions between teams [20].
Besides decision-support or as a previous step to it, agent-based systems can also
be used to simulate the crisis response and its coordination. This role for modelling
and simulation has been recognized for decades as a contribution to planning and
evaluating response strategies [21]. The value of simulation, as stated in the
introduction to this paper, lies in the difficulty or unfeasibility of carrying out
experiments in real-life [2]. On one hand, MAS allow designing controlled
experiments while at the same time offering the scalability needed for adding (or
deleting) roles and rapidly redefining the response organization [7, 21-23]. On the
other hand, one of the main uses of agent-based simulation is studying the emergent
behaviour of the crisis response organization through the interaction among
participating agents [7]. Both capabilities fit well with the nature of a crisis response
organization and specifically with coordination as a study objective.
The rest of this paper is structured as follows. Section 2 provides the methodology
behind the development of the MAS, first using GAIA [24] and then an
implementation-dependent design with GAIA2JADE [25], where JADE is the
underlying agent framework [26]. Section 3 shows the results of the analysis phase.
Section 4 presents the high-level design (architecture) of the MAS. Section 5 relates
to the detailed design and its transition to an implementation-dependent model.
Section 6 presents a final discussion and the next steps in this research.
2 Methodology for Developing the MAS
For the analysis and design of our MAS, we have chosen the widely used GAIA
methodology [24], which views a MAS as an organized society of individuals in
which each agent plays one or more roles and has one or more responsibilities. Each
Proceedings of EOMAS 2009
agent interacts with other agents according to a set of protocols and these interactions
are seen as the way the agent accomplishes her role in the system.
The GAIA methodology is implementation-independent, which means that it is
aimed at analysis and design models. A graphical depiction of the models that should
result from following the GAIA methodology is shown in Fig. 1.
Collection of
Requirements
Requirements
Environmental
Model
Preliminary Preliminary
Analysis Role Model Interaction Model
Organizational
Rules
Organizational
Architectural Structure
Design
Role Interaction
Model Model
Detailed Agent Services
Design Model Model
Implementation
Fig. 1. GAIA Methodology (process and models) adapted from [24]
Because the GAIA methodology is implementation-independent, a transition is
expected between the GAIA-based analysis and design of the MAS and an
implementation-dependent design. We have chosen JADE [26] as the Java-based
agent development framework, due to its widespread adoption, available
documentation, open source character and compliance with the FIPA (Foundation for
Intelligent Physical Agents) specifications [27]. Also, JADE is one of the frameworks
that have already been used for developing MAS in the field of crisis response [17,
22]. We use the GAIA2JADE process [25, 28] as a guide to transform and continue
the GAIA method into a JADE-dependent modelling and implementation of the
MAS. According to this process, after finishing the GAIA methodology, there are
some steps to continue into a JADE-based development, as shown in Table 1.
Proceedings of EOMAS 2009
Table 1. GAIA2JADE Process, adapted from [25].
STEP INPUT OUTPUT COMMENTS
Define GAIA Interactions Domain Messages should comply with
communication Model Ontology; ACL FIPA ACL message structure.
protocols Messages Sequence diagrams may
contribute to modelling.
Define GAIA Environmental, Application Data Domain ontology classes are
activities Interactions, and Class Diagram; represented as JAVA classes.
refinement Roles Models; JADE Activities Algorithms are documented for
table Domain Ontology Refinement each liveness property.
Table
Define JADE GAIA Interactions JADE Behaviours Coding of behaviours in JADE:
Behaviours and Roles Models; Repository (1) behaviours are defined; (2)
JADE ACL Messages, State diagrams are created for
Application Data each behaviour; (3) constructors
Class Diagram, and are created; (4) behaviour action,
Activities input and output are defined; (5)
Refinement Table behaviour functionality is added.
Define Jade GAIA Agent and JAVA Code of All events should be caught in
Agents Service Models; Agents (in JADE) this level.
JADE Behaviors
3 Analysis of the MAS
Before the GAIA based analysis, we went through an initial phase of requirement
elicitation, based on identification of response processes for a particular crisis
scenario. The processes were extracted from crisis response manuals in The
Netherlands [29] and the scenario was adapted from a training case used to describe
the Dutch crisis response levels. By using a particular scenario, we were able to limit
the number of relevant processes, according to an additional document of guidelines.
The result was a list of response procedures and the agencies involved, which served
as the basis for identifying the roles for the agents.
The basic response processes are classified according to the responsible discipline,
we will focus on this paper in the fire response processes summarized in Table 2.
Table 2. Fire service processes, adapted from [29].
A. SOURCE AND EFFECT CONTAINMENT
Responsible actor: Fire services (regional commander)
1. Fire fighting and containment of dangerous substance emissions
2. Rescue and technical assistance
3. Decontamination of people and animals
4. Decontamination o vehicles and infrastructure
5. Detection (observation) and measurement
6. Warning the population
7. Clearing and providing access
Proceedings of EOMAS 2009
The crisis scenario is a fictitious accident developed for training purposes to
illustrate the scaling up of the crisis response as an incident progresses. It goes from a
routine response through the four scales of a coordinated response according to the
Dutch GRIP (Coordinated Response Procedure) levels. Table 3 describes the scenario
from the beginning until it reaches GRIP level 2, because this level is enough to study
multidisciplinary coordination without increasing the complexity of the model.
Table 3. Crisis scenario scaling up.
PHASE DESCRIPTION
Phase 0 The scenario starts with a crane doing work on a road in the jurisdiction of a
given municipality.
Phase 1 The incident starts when a truck, carrying flammable liquid, crashes onto the
(Routine) crane. This prompts the response of fire, police and ambulance services in what
is initially a routine situation.
Phase 2 Escalation of the incident occurs when the truck catches fire. The incident
(GRIP 1) becomes larger than originally assessed, more response units are needed and a
coordinated response is required from multiple disciplines which will setup a
CoPI (Commando Plaats Incident) operational team, and maintain the mayor of
the municipality informed of the situation.
Phase 3 Further escalation occurs when the flammable liquid leaks, the fire spreads and
(GRIP 2) comes into contact with the neighbouring municipality (close to a city). This
requires a single leader coordinating the response and that two additional teams
be setup, a tactical and a strategic team. The incident is now a regional concern.
3.1 Environmental Model
The environmental model in GAIA is an abstract, computational representation of the
environment in which the MAS will be situated. Although GAIA does not provide
specific techniques, it can be shown as a list of resources characterized by the type of
actions that agents can perform on it [24]. Table 4 shows the resources in the crisis
scenario.
Table 4. Crisis scenario resources as a basis for the environmental model.
CRISIS PHASE RESOURCE TYPE OF ACTION
0 Road, Obstacle, Housing Readable
Vehicles Changeable
1 Vehicle, Victims, Civilians Changeable
2-3 Fire Changeable
It should be noted that the environment itself is later modelled as a discrete-event
simulation model, for which these resources would be the entities. Such model is
outside the scope of this paper, which focuses on the MAS organization of the crisis
response and not on the simulation of the crisis environment.
Proceedings of EOMAS 2009
3.2 Preliminary Role Model
The preliminary role model provides an analysis phase view of the roles and protocols
in the MAS, where roles are represented with permissions and responsibilities [24].
Again, we will focus on fire containment due to space considerations. Permissions for
the Fireman and OvD (Officier van Dienst, Fire Chief) role are presented in Table 5.
Table 5. Roles and Permissions.
ROLE PERMISSION RESOURCE
Fireman / OvD reads Road, Obstacle, Housing, Vehicle, Civilian
changes IncidentVehicle, Victim, Fire
Responsibilities are expressed in terms of liveness properties that describe the state
of affairs that an agent must bring about. They are expressed as expressions
containing activities (underlined) and protocols (activities that require interaction with
other roles – not underlined). Using “x*” means that the activity occurs 0 or more
times; “x║y” means that the activities x and y are interleaved (occur in parallel).
Liveness properties of the Fireman and the Fire Officer are shown in Table 6.
Table 6. Liveness properties.
ROLE LIVENESS PROPERTY
Fireman = (AssessFire.InformFireAssessment.ContainFire)*║
(IdentifyVictims.InformVictimLocation)*
OvD = (AnalyseFireSituation.PlanContainment.CommunicateContainmentPlan.
GetContainmentResources.DeployContainment.SuperviseContainment)*
3.3 Preliminary Interaction Model
The preliminary interaction model captures the dependencies and relationships
between the various roles in the MAS organization [24]. Each interaction protocol is
defined in terms of: name, initiator, partner, inputs and outputs. The protocols for the
liveness properties in Table 6 are shown in Table 7.
Table 7. Preliminary Interaction Model.
PROTOCOL NAME INITIATOR PARTNER INPUT OUTPUT
InformFireAssessment Fireman OvD Site assessment Message to
commander
InformVictimLocation Fireman OvD Site assessment Message to
commander
AnalyseFireSituation OvD Officers Nature, scope and Fire analysis
expected evolution
PlanContainment OvD Officers Fire analysis Containment plan
DeployContainment OvD Fireman Containment plan, Resources deployed
resources received
Proceedings of EOMAS 2009
4 Architectural Design of the MAS
A MAS architecture is equivalent to its organizational structure, which in turn is a
result of combining the system topology and the control regime [24].
4.1 Organizational Structure
A topology for the MAS organizational structure may be peer-to-peer, hierarchical,
multi-level or composite. Given the initial discussion of a crisis response
organization, topology in this case needs to combine the hierarchy explicitly designed
into the response disciplines, with the lateral relationships possible between first
responders and commanders. The control regime can be based on specialization or
partition. In a crisis response organization (homogeneous) partitioning occurs within
disciplines and (heterogeneous) specialization occurs in between disciplines. The
resulting structure is depicted semi-formally in Fig. 2. Besides the role of Fireman and
OvD shown above, this structure also includes equivalent structures of other roles not
shown due to space considerations: for the police (Policeman and Police Chief, OvD-
P), medical services (medics and Medical Officer, OvD-G) as well as regional
commanders for each discipline (CvD, Commander van Dienst) and an overall
Operational Leader (OL).
OL
Depends Control Control
Peer
Peer Peer
CvD CvD-P CvD-G
Control
CL
Control Control
Depends Control Control
Peer
Peer Peer
OvD OvD-P OvD-G
Control Control Control Control Control Control
Peer Peer Peer Peer Peer
Fireman [1] Fireman [n] Policeman [1] Policeman [m] Medic [1] Medic [o]
Peer
Fig. 2. Organizational Structure of the MAS
4.2 Role Model
After having defined the organizational structure, the preliminary role model can be
revised, resulting in a detailed role model for each of the final roles. To illustrate a
role model with one example, Table 8 shows the Fireman role.
Proceedings of EOMAS 2009
Table 8. Fireman Role.
ROLE Fireman
DESCRIPTION The Fireman is the Fire Services field agent in charge of fighting
(suppressing) fires and rescuing victims.
PROTOCOLS & GetToLocation, NotifyArrival, AssessSituation, InformAssessment,
ACTIVITIES UpdateAssessment, InformResult, ContainFire, MoveVictim
PERMISSIONS Read Civilian, House, Vehicle.
Change Fire, Responder (proxy simulated fireman: self)
RESPONSIBILITIES Fireman = GetToLocation. NotifyArrival.(AssessSituation.
InformAssessment.(Respond. UpdateAssessment. InformResult)*)*
Respond = ContainFire | MoveVictim
4.3 Interaction Model
The interactions model represents the interaction between the agents, connected
through input/output. The fire containment protocols are shown in Fig. 3. Although
the model is sequential, interactions are shown between initiating (left of each box)
and receiving agents (to the right). Dotted arrows represent conditional transitions.
After Communicate Plan, other actions continue, but are left out for lack of space.
RequestAssessment
EstablishCoPI (conditional)
OvD Fireman Input
OvD OvD-P, OvD-G Shared situation
Request information pertinent to fire Request sent awarness
containment. Although the other officers can prompt CoPI and CL
GRIP level to increase, it is up to OvD to established
set it up and a CoPI leader along with it.
InformAssessment
CallForProposals (conditional)
Fireman OvD Input
OL OvD, OvD-P, CoPI
Sends message to OvD with current Message sent OvD-G established
situation awareness.
CoPI leader requests plan proposals from Cfp sent
all disciplinary leaders (officers) until
agreement is reached.
MultidisciplinaryConsultation
OvD OvD-P, OvD-G Fire related situation
awareness CommunicatePlan
Sharing of mono-disciplinary situation
Shared situation
awareness to obtain inter-disciplinary
awareness OvD OvD-P, OvD-G Committed Plan
situation awareness.
Once a plan is committed, the OvD Message sent
communicates it to other officers.
Fig. 3. Interaction Model for Fire Containment
Proceedings of EOMAS 2009
5 Detailed Design
This section contains the detailed design of the agent-based aspects in the form of an
agent and a services model.
5.1 Agent Model
The agent model defines the agent classes that will play specific roles [24]. In our
case, the OL role is absorbed by the CvD agent (in practice this is what usually occurs
in an emergency). Similarly, the CL role is absorbed by the OvD. All commanders
and officers will have only one instance. Following the notation suggested in [28], the
agent model is presented in Fig. 4 where blocks represents agent types, rounded
figures represent roles and “*” means 0 or more instances.
OL CvD CvD-P CvD-G
CvD CvD-P CvD-G
CL OvD OvD-P OvD-G
OvD OvD-P OvD-G
Fireman* Policeman* Medic*
Fireman Policeman Medic
Fig. 4. Agent Model
5.2 Communication Protocols
The first implementation-dependent step that follows the GAIA2JADE process [25] is
defining the communication protocols for the agents, through an ontology and a set of
ACL messages. The domain ontology in Jade describes the elements that agent use to
Proceedings of EOMAS 2009
create the content of messages, specifically concepts, predicates and actions [26].
Concepts are the semantic elements of the vocabulary. Predicates are the structural
elements. Actions are special concepts that denote agent actions. Table 9 describes the
domain ontology (omitting the attributes).
Table 9. Domain Ontology.
ELEMENT NAME DESCRIPTION
Concept Civilian Civilian (victims or not)
Concept Estimated Population Population observed by a responder
Concept Fire Observed fire
Concept Infrastructure Element Housing, object, vehicle or obstacle
Concept Location Defined location
Concept Resource Material response resource
Concept Responder Information about a responder
Concept Strategy Response strategy
Concept Time Timestamp of observations
Concept Traffic Perceived traffic
Predicate Situation Assessment Current observation of the incident
Action Alarm Alarm message
Action Plan Response plan per discipline
Given the interaction protocols defined in the GAIA design and the above
ontology, ACL messages according to FIPA [27] can be defined. As an example,
Table 10 presents the interaction protocol for RequestAssessment as a FIPA Query.
Table 10. RequestAssessment FIPA Query.
ACL MESSAGES SENDER RECEIVER FIPA PERFORMATIVE
Request Assessment Officer Responder Query-ref
Query Not Understood Responder Officer Not understood
Refuse Query Responder Officer Refuse
Query Failure Responder Officer Failure
Inform Assessment Responder Officer Inform
5.3 Activities Refinement Table
This step defines the activities refinement table, where application-dependent data,
their structure and the algorithms that are going to be used by the agents are defined
[25]. The table is meant to specify the liveness properties of the agents, having
defined the ontology. Under read and change, there is a reference to data classes (no
longer environmental objects, but ontology-dependent classes). Under Description
there is a top-level algorithm in pseudocode for the corresponding activity. As an
example, Table 11 shows the activity refinement table portion for the Fireman role.
Proceedings of EOMAS 2009
Table 11. Activity refinement table for Fireman role.
ROLE ACTIVITY READ CHANGE DESCRIPTION
Fireman Fireman Resource - do GetToLocation
Weather do NotifyArrival
Responder while Strategy.exit != [exit criteria]
Location do AssessSituation
Element do InformAssessment
Fire while fire != null || civlians.status != victim
Civilian do Respond
do UpdateAssessment
do InformResult
end while
end while
Fireman Respond Fire if assigned Fire
Civilian do ContainFire
else if assigned Victim
do MoveVictim
end if
5.4 Jade Behaviors
This step implements Gaia activities as Jade Behaviours [25]. First, behaviours are
defined. Second, a state diagram (UML) is provided for each relevant behaviour to
help identify data exchange between behaviours and easily map to Jade FSM (Finite
State Machine) behaviours. Jade behaviours are defined from Gaia activities, through
mapping activities. All Gaia liveness formulas are translated to JADE behaviours. In
this case we use the one for Fireman defined in the Responsibilities section of Table
8. As a general rule, the “•” operator in a liveness formula denotes that the behaviour
at the left-hand side is complex, while the [], +, *, | operators denote that the left-hand
side can be a finite state machine. All behaviours should inherit from the
jade.core.behaviours.Behaviour class. As an example, we provide the FSM diagram
in UML for the Fireman agent in Fig. 5. Implementation follows from the bottom-up
(from simple to complex behaviours). This results in one FSM diagram for each agent
which is subsequently implemented as FSM and FSM Child Behaviors in JADE.
Proceedings of EOMAS 2009
Fireman
Respond
Fig. 5. FSM diagram for Fireman agent.
6 Conclusion
This paper summarized the processes of analysis and design of a multi-agent system
for a crisis response organization with the purpose of building a simulation testbed to
experiment with different coordination mechanisms. With respect to the use of the
GAIA methodology, it proved to be a structured way of performing analysis and
design. In this case, Organizational Rules and a Service Model were not needed, due
to the fact that the agent services are not meant for “consumption” but rather for
simulation. They could be specified in the future so the same agent behaviour could
be used not for simulating crisis response agents, but rather to support real crisis
response agents with information processing tasks.
The transition from a GAIA-based analysis and design to a JADE-dependent
design proved to be relatively straightforward through the use of finite state machines
and corresponding JADE behaviours. Indeed, the FSM representation for the agents
was chosen due to the facilities offered by JADE, but it could be seen also a source
for formal models of the agents that could directly be simulated. In addition, having
expressed the interaction protocols with ACL messages enables implementation of
agent communication in accordance with FIPA specifications. This allows reusing
and complying with a predefined set of interaction protocols.
Running simulation experiments with this model will permit comparing between
coordination strategies in terms of their effectiveness (damage reduction and
Proceedings of EOMAS 2009
protection of civilians) and efficiency (performance and response time). By running
the agents repeatedly over the same scenario, making variations over the coordination
mechanisms as expressed through interaction protocols, through centralized vs. peer
to peer communication and through standards (embedded in the FSM structure) vs.
emergence (occurring when agents behave autonomously), the simulation can be used
to experiment and analyse different configurations that can be used to inform
coordination theory in crisis response or as basis for developing ICT services that
support responders in the field.
After the experiments and validation, this research will also be able to show results
with respect to the integration of MAS and discrete-event simulation in a single
model, the (dis)advantages of JADE for simulation purposes, and the rigidity that may
arise from using GAIA: we consider these to be interesting points for future research.
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