1. business and industrial
2. energy
3. renewable energy

A Survey on Multi-agent Management Approaches
in the Context of Intelligent Energy Systems
Khawla Ghribi
a,b
Email: [email protected]
Sylvie S. Ghalila a,c
Email: [email protected]
Zahia Guessoum
b
Email: [email protected]
Javier Gil-Quijano c
Email: [email protected]
Dhafer Malouche
d,e
Email: [email protected]
a: CEA-LinkLab, Pôle Technologique El Ghazela, 2083 Ariana, TUNISIE;
b: LIP6-UPMC, 75015 Paris, FRANCE;
c: CEA-LIST-LADIS, F-91191 Gif-sur-Yvette, FRANCE ;
d: Département Statistique, ESSAI, Charguia II, B.P 675 - 1080 Tunis, TUNISIE;
e: U2S-ENIT, B.P 37, 1002 Tunis, TUNISIE.
Abstract—Several papers deal with the topic of multi-agent
solutions for the energy management in complex energy systems.
According to the time scale, we distinguish two kinds of management: a reactive management (short-term) and an anticipative
management (long-term). The objective of this paper is to propose
a survey of the different multi-agent approaches, we thus propose
a classification of existing management approaches according to
the level of the system at which the decisions are made.
The physical deployment of services’ steering shows that these
services can be only of two types: independent and interdependent and gives hints about the adequate tools of management.
Index Terms—Multi-agent; models; deployment; management;
decision.
I. INTRODUCTION
An energy system is often considered as a multi-source
system composed of at least one production unit and optionally
a unit of storage. The label energy systems covers systems of
different scales: it designates single building equipped by a
production unit and may designate districts and distribution
networks. A production unit may be based on renewable
energy.
Intelligent energy system is an energy system which is able
to automate tasks usually done by humans. In the case of
renewable energy sources, they allow the minimization of
energy lost in transmission and provide high quality energy
through the efficient management.
Optimal management of complex energy systems based on
renewable energy must take into account both the variability
of the available resources (weather) and the interaction of
the energy system with the distribution network: it deals
with the availability / relevance of energy producers, physical
constraints, network requirements and economic constraints.
This optimal management is materialized by taking production
commitments (via medium-term contracts or participation in
daily markets), developing consumption schedules (on time
scales ranging from 10 to 60 minutes) and periodically updating these production commitments and consumption sched-
ules. Instructions extracted from these schedules and production commitments are used by control mechanisms operating
at time scales ranging from milliseconds to the minute which
ensure “instantaneous “ stability and security of the energy
system.
Various studies have been proposed for the management of
complex energy systems.
The first approaches of management aim to automate tasks
by mainly learning humans preferences [1], [2], [3]: Mozer
[1], use neural networks models to manage energy within a
building.These models anticipate the residents’ needs, after
being trained by observations of residents’ lifestyle.
There exist, subsequently, approaches which are based on
one central unit monopolizing the management of the energy
system, [4][5]. In [4] and [5], the authors delegate the process
of management to a central energy management system (EMS)
which selects one of predefined algorithms returning a power
production planning after performing a power prediction and a
load forecasting. In these two papers, the real-time adaptation
to new constraints, is delegated to local controllers. The latter
have the ability to reroute power in a limited offred margin.
Adding local controllers to the management system is indispensable and aims to improve flexibility. However, the above
mentioned approaches still suffer from a lack of flexibility illustrated by difficulties when adding or removing equipments.
Compared to classical centralized solutions, Multi-Agent Systems (MAS) naturally provide flexibility and distributability.
For those reasons, the use of MAS for management of energetic systems is fast increasing.
MAS have been widely used in the design and the implementation of smart management of complex energy systems
[6-18]. Those different works aim to:
•
anticipate the different components (generators, consumers, storage) behaviors and develop power planning
for possible situations [9], [14]. For instance, Nagata
•
•
and al. [9] propose a market-based mechanism, where
a central agent initiates a call for proposals every x
minutes. Agents, other than the central agent, send messages of purchase or sale of electrical power based on
demand’s prediction (in the case of load agents) and
generation’s prediction (in the case of producer agents).
After a negotiation process, the central unit determines
the operation settings of the system for the next period.
The occurrence of new constraints such as a low battery
level between two calls for proposals doesn’t modify the
planned operation of the system.
ensure a reactive management[12], [13], [15], [16], [18].
In [12] the power mistmatch (the difference between
consumed and produced energy in the whole system) is
determined by aggregation of local mismatches. Depending on the found value of the power mismatch, decisions
such as cut off some loads (loads with the lowest priority)
are taken in a distributed and reactive way. While in
[13] agents in charge of producers and agents in charge
of consumers communicate, exchange commands and
information and monitor in time their corresponding
producer/consumer based on the announcement provided
by a control agent such as electricity price announcement
or the occurrence of an upstream outage. Smitha and
Fossy [18], use fuzzy rules to reactively control critical
loads.
combine both anticipative and reactive management [68], [11], [17], [19]. For instance, in [17], agents in charge
of consumers predict future consumption and send it
to their redistribution agent. The redistribution agent,
which manages a cluster of consumers, reacts to an
overconsumption (higher than predicted) of a consumer
by redistributing the available resources within the cluster.
Because of the numerous management solutions available
in the state-of-the-art, choosing the best management
strategy for a given energy system is an issue. Our
objective is to provide guidelines to facilitate the selection
of the management strategy. To reach our objective we
provide in this paper a classification of different management strategies that are suitable to different physical and
software situations.
The remaining parts of the paper are organized as follows.
In Section 2, we address the problem of the scarcity
of publicly-available work that aim to test the research
contributions in real energy systems, summarize from
available work the physical deployment of smart buildings, and explain the influence of time constraints and the
nature of needed services on the choice of the adequate
tools of management. Section 3 presents the multi-agent
models. In Section 4, according to the level of the
system at which the decisions are made, a classification
of existing multi-agent approaches is introduced. Section
5 is dedicated to a discussion about generated classes.
Section 6 concludes.
II. INTELLIGENT ENERGY SYSTEMS: PHYSICAL
DEPLOYMENT AND MANAGEMENT
A. Intelligent energy systems: Physical deployment
Despite the importance of research in the domain of energy systems management, we note the scarcity of publiclyavailable detailed works that aim to test research contributions
in real energy systems. The scarcity of detailed data sets is
considered as an obstacle to academic research. In addition
to the rarity of complete databases, we note the lack of
information about the deployed configuration allowing the data
collection. Providing information about the deployed configuration facilitates the development of test platforms. Therefore,
we propose to describe in this section the existing deployment
solutions enabling easy assessment of management based on
real data.
To simulate real building (as example of small-scale energy
system), monitored loads in the test platforms must be of
various types: loads with highly variable power usage (e.g.
Tvs, Computers), stable loads (e.g. digital clocks), indicative
loads (e.g. refrigerators), continuously functioning loads (e.g.
Modem, desk phones, refrigerators), loads functioning for few
minutes per day (e.g. oven, microwave, kettle, coffee maker),
and in between loads that work from 1 to 2 hours a day (e.g.
smart phones chargers, video game consoles, laptops).
Abras and al. [20] have proposed in the project “Smart*”
a description of the infrastructure used to gather data: a
commercially available hardware and open source Linux-based
software to communicate with used hardware. We summarize,
in the following, tools needed to realize the smart building
without going into details of the equipments’choice.
Figure 1.
Monitored smart building
Wall switches, which control lighting and exhaust fans,
are replaced by switches that send notifications of on/off
events to a remote server. These events may also include
the event Dim: The change of light level. Electrical outlets
are monitored by plug-meters. Plug-meters are intermediate
between the unit (oven, computer...) and the electrical outlet.
Choosing suitable plug meters should take into consideration
the variation of the power use: the plug-meters which are
suitable for stable loads are different from plugmeters that
monitor devices with highly variable power use.
Wall switches and plug-meters send signals to a modem and
thereafter to a remote server: the sent messages are on/off
notifications for wall switches and the amount of consumed
energy for plug-meters. Wall switches send notifications to
server every preset duration in seconds, while the consumed
energy recorded by plug-meters will be requested by server
every x seconds (the value of x depends on the device [21]).
In addition to the use of electricity (see Figure 1), environment
data as indoor temperature, outdoor temperature, rain rate,
indoor humidity, outdoor humidity and wind speed will be
transmitted to the remote server. These data are captured by
sensors installed in the rooms, outside the building and in the
refrigerator.
The detailed dataset requires, adding to electrical and
weather data, information about heating and cooling systems.
Therefore, communicating thermostats are used: thermostats
send data to the server through several radio technologies
(WiFi, ZigBee, Z -Wave). These data are On /Off events and
the change in the temperature set values.
A smart building usually contains motion sensors that send
notifications to the modem when motion is detected or when
a preset value of minutes have been spent without detected
movement. These type of data are used to take into account
the user behavior.
Kolter and al. [22] propose a freely available dataset and the
hardware used to obtain this dataset in order to tackle the
problem of energy disaggregation (determining the component
devices from an aggregated electricity signal). There are three
levels of monitoring: plug level, circuit level and whole-home
level. Plug level is represented by power strips (see Figure
2). Each power strip connects appliances to the home internet
connection via a router. Circuit level is a set of appliances
performing the same role, (e.g. the kitchen outlets, lighting...)
monitored by eMonitors which are attached to the house’s
circuit breaker panel. The last monitoring level is the whole
home monitoring which is an association of a transformer,
an oscilloscope and a converter that ensure respectively the
measure of current, voltage and the transformation of analog
signal to a digital reading.
Electronics which monitor the circuit level and the home
level of data collection can be regrouped in a box. The box is
connected to a laptop and to an external hard disk. The data
logged in the hard disk of each box are transferred manually
or via the network to a database server.
We cannot tackle the issue of the intelligent energy
systems deployment without addressing the topic of the
estimation of renewable energy generation. Indeed the current
worldwide ecological and political context drives towards
a fast integration of renewable energy. The integration of
renewables, and especially solar and wind energy causes a
number of issues, particularly associated to variability and
intermittence.
Figure 2.
Power Strip [22]
In that context researchers were interested in the development of models which automatically and accurately predict
renewable generation. For instance, Sharma and al. [23] and
Lopez and al. [24] propose models to estimate solar radiation
(used as proxy to solar generation) from weather metrics.
B. Intelligent energy systems: Management
Intelligent energy systems aim to maximize the comfort of
humans by optimizing tasks needed to be provided in a specific
time interval. This is only possible through the use of efficient
tools of management.
To choose the most efficient tools of management, we must
go through a specification of the required management. When
considering temporal constraints, we distinguish two types of
management (for a complete explanation, see [8] and [25]):
• A reactive management applied to a set of pressing tasks.
Pressing means that a violation of its time constraints can
block the whole system , such as the task of switching from
battery to a backup generator in case of low battery level.
• A proactive management functioning on long periods of
time and operates with average energy values. A proactive
management is at most suitable for permanent services.
We mean by service (as it has been defined in [8]) a response
to a specific need of the user realized by a set of equipments
and including a set of tasks (e.g. the heating service which is
the response to user’s need of keeping room’s temperature in
a specific interval and which is realized by radiators).
The reactivity and the proactivity characterize the service’s
management. When considering the whole system, we notice
that there is not a totally reactive management neither a proactive one, it is in fact a combination of these two management
types.
The choice of tools to manage the whole energy system
depends on the services provided by the system.
A permanent service is a service whose energetic activities
occur over a long interval of the operation horizon of the
whole system, such as the solar production service offered
by PV panels. In contrast to temporary services which have
a limited horizon of operation, such as the washing service
offered by the washing machine and the dryer.
Temporary services are characterized by an operation interval = [Time of earliest start, the latest end time], while
permanent services are characterized by an impact: the impact
of a permanent service is the time required to move from
a critical situation where the user comfort is not satisfied
(a function of satisfaction quantifying the user comfort is
beyond a certain threshold) to a situation of total satisfaction.
The ranking of these specifications (Operation intervals of
temporary services and impacts of permanent services) in
the operation horizon of the whole system creates disjoint
and intersectional intervals and allows to conclude about the
independence of services. Services corresponding to intervals
which are disconnected from the rest of intervals, have a
high level of independence. In contrast to services whose
characteristic intervals are highly interfered, which have a high
level of dependence.
The nature of services (Independent, dependent, permanent
,temporary) gives hints about the required management. Indeed, the independence of a service from the rest of services
promotes autonomy and subsequently promotes self control.
In this case the distributed paradigm seems to be an efficient
solution.
The management processes are completed by a local control
process which is materialized by a set of local controllers
installed at the operating points of power-electronic interfaces
of energy producers and provide a very fast regulation to an
electric perturbing event. e.g. : voltage regulation. This local
control is out of our interest in this paper.
III. MULTI-AGENT MODELS
A Multi-Agent System (MAS) is a set of active and
autonomous units in interaction, able to be organized in a
dynamic and adaptive way.
Agents are autonomous, able to react, they can represent physical or virtual entities, are located in environment characterized
by a temporal persistence to satisfy their objectives depending
on their resources and skills.
The MAS approach propose interesting characteristics for the
development of a system composed by autonomous multiple components that can cooperate. Agents allow modelling
heterogeneous, complex, dynamic, non-linear and evolutive
systems. Moreover, they allow showing intelligence and capacities which are different and globally higher than those
of the individual composing agents. This intelligence emerges
from the coexistence of roughly autonomous agents that are
able to cooperate.
The MAS paradigm draws its basis from different fields
(e.g. software engineering, artificial intelligence, current and
distributed programming, etc.), this pluridisciplinarity reveals
its consistence but induces a big complexity and variety of approaches. Thus, different agent models (their main categories
are reactive and cognitive agents) of environment, interaction
and organization are elaborated and always combined to build
a MAS.
The application of MAS for complex environment control
reveals different problems which are still the object of several researches. In fact, the complexity of new information
systems (e.g. accessible system from internet), the traditional
application seen under MAS (e.g. logistic, transport, games,
etc.) and the recent emergent applications (smart buildings,
ambient intelligence) are considerably increasing due to their
distribution, the big amount of information handled, their
cooperative and adaptive aspect, and their openness.
We present in this paper an overview of multi-agent application in complex energy systems’ fields. These systems have all
the characteristics of a good application field of multi-agent
system. They allow in fact validating and demonstrating the
proposed multi-agent models limits.
IV. MULTI-AGENT MANAGEMENT APPROACHES
The intelligence of energy systems is strongly related to the
capacity to anticipate behaviors, in other words the ability to
predict the occurrence of an event and the planning of actions
that follow this occurrence.
We focus in this paper on multi-agent approaches which
manage the piloting plans.
Several multi-agent solutions have been proposed to define
and update the piloting plans. A spectrum of planning methods can be built. Level of decentralization varies along this
spectrum from very high where we find the fully decentralized
approaches of planning to a very low where decisions are concentrated in a central unit. This spectrum can be decomposed
into three main supersets:
1) Centralized decision making for distributed execution,
2) Distributed decision making to achieve an overall objective,
3) Distributed decision making for distributed execution.
A. Centralized decision making for distributed execution
A plan is generated in a centralized manner by a central
agent. Therefore the generated plan is partially ordered plan.
The central agent divides the generated plan under potentially
synchronized sub-plans and transmits the sub-plans to the
executors agents (see Figure 3). Executors agents perform their
plans as concurrent processes [26].
Figure 3.
Centralized plan divided in sub-plans
The proposed method in [6], which manages energy within
a building, falls into this group of methods. The Smart building
multi-agent system is modeled by four types of agent :
-Sensor agents : each sensor agent is in charge of a set of
physical sensors,(e.g. the level of light, motion)
-Effector agents: effectors have a direct impact on device
behaviors.
-Butler agent: Butler agent is the central agent where main
decisions are made.
-Housekeeper agent: This agent gives a repertory of the
active agents and its capabilities.
Physical sensors send in real-time the gathered data to the
corresponding sensor agent (SA). The SA affects a symbolic
representation to the centigrade gathered data. This abstraction
mimics the way in which humans think.
Example : Values of temperature are transformed to “ warm
“ and “ cold “. A set of rules which describe the physical
environment are established at this level of reasoning.
Example : Cold (X) : T<18 for a given situation X.
The butler agent transforms observations of a current situation
to logic formulas. These logic formulas can be used to extract
explicit information by a deduction process. The butler agent
consults the set of available rules in order to specify goals.
Rules have the form of
<Goal> : <Pre-conditions for these goals to be detected>
Example :
ImproveHealth(x) : present(x,y), user(y), has_fever(y).
which represents the rule: if (In a situation X, a user Y is
present and has fever) then the goal “ImproveHealth” should
be achieved.
This method is based on the existence of a workflow repository
(Patterns of activities): periodically or as an answer to a user
action, the butler agent selects the most appropriate workflow
to the current situation by semantically matching the goal
of the user and the profiles of all the workflows available
in the knowledge base of the system and choosing the most
consistent with the goal.
The semantic matchmaking is a hierarchical process which
can be applied within a workflow to find the most appropriate
subflows.
Once a detailed workflow (which is composed of simple goals
that can be satisfied by effectors ) is found, the process of
semantic matchmaking stops. In this step, the builder agent
consults housekeeper agent to allocate simple goals/actions to
the right agent. To accomplish simple goals, effectors take
decisions relative to the question “ how to fulfill the simple
goals concretely?”
Example: If the goal is to reduce temperature, the effector
which is in charge of controlling temperature, chooses between
turning on the air conditioning or opening the window. In case
of interactive effectors, hints helping effectors to fulfill simple
goals can be sent from users.
SCADA (Supervisory Control and Data Acquisition) systems are centralized systems used to monitor and control
equipments in the industrial sector. Its major function is to
gather data from remote equipments and provide an overall
control [27].
All approaches, managing complex energy system and using
anticipation of events by means of SCADA systems, belong
to this class of management. Among these approaches, we
find the method used in [7] that addresses the problem of
management in a residential grid. The central agent of [7]’s
MAS defines goals and associates to each goal a plan of ac-
tions transforming a current situation to another one satisfying
the goal. Moreover, it proposes a solution to the problem of
uncertain gathered data based on the probabilistic theories.
B. Distributed decision making to achieve an overall objective
The synthesis of plans is distributed on several agents.
Each agent which is in charge of a sub-task generates the
corresponding sub-plan before starting an interaction phase.
The goal of the interaction phase is the development of a
global plan (see Figure 4). Interaction phase may involve the
exchange of sub-plans to synchronize or to refine, [26]. This
class of methods which is based in the convergence of agent’s
behaviors toward a goal can be described in a semi distributed
manner. It differs from the centralized approaches by distributing the resolution of the energy management problem and
cannot be described as fully distributed due to the existence
of an agent that orchestrates the execution.
Figure 4.
Development of global plan from sub-plans
This class includes the approach proposed in [8] in which an
agent manages a set of domestic equipments and is responsible
of a precise service. A service, as defined in II.B is a response
to a specific need of the user realized by a set of equipments.
A service is divided into stages. Each agent generates a local
plan that does not violate its constraints. A local plan is a
possible cutting of a service (Heating service for example)
into set of steps, each step is characterized by a duration and
by an amount of power. A step can be empty.
The local plan is valid for the period between k and k+l, l
is the time horizon for planning and is characterized by a
degree of user satisfaction. The method consists in developing
a “Solving agent” with high computing resources whose role
is to build a global plan from local plans. Initially, each agent
generates N local plans leading to a maximum level of satisfaction then sends it with its satisfaction to the “solving agent”.
The “Solving agent” tries for m iterations to find a feasible
global plan offering the maximum possible satisfaction. In case
of failure, the level of satisfaction is decreased by one unit of
satisfaction and the “Solving Agent” resumed the search of a
feasible solution in m new iterations.
Deindl and al. [28] proposed as well a semi distributed
multi-agent approach of large electrical grids’ management.
This approach is based on the resources allocation. The
MAS is composed of consumer agents (buyers) and producer
agents (sellers). Authors provide consumer agents with a high
computing capability, this capability allows them to elaborate
Figure 5.
Parallelization of sub-plans
local plans for future slots of time.
Consumer agents develop propositions of local plans for future
slots based on forecasted energy demands and estimation of
electricity prices, then they participate to one or multiple
negotiations (each consumer can participate to multiple negotiations simultaneously). Each negotiation includes producer
and consumer agents seeking to find an optimal allocation
of energy for a specific time slot. Negotiators can change
significantly their propositions (by shifting load demand of
consumers agents to later slots of times) in order to achieve
an overall goal: the optimal allocation of energy.
The existences of this overall goal as well as the absence of an
explicit controller allow us to qualify this approach by semi
distributed.
This class of approaches includes also mechanisms used in
[9], [14] and [17].
C. Distributed decision making for distributed execution
Agents can plan and execute their plans regarding of the
existence of an overall plan or an overall goal of the system.
They proceed an interaction phase in order to parallelize
competitors sub-plans but not in order to establish a global
plan (see Figure 5). One of the proposed approaches to
parallelize sub-plans is the incremental approach: To consider
a set of coordinated sub-plans and to insert a new sub-plan to
coordinate with existing plans, [26].
The method proposed in [10] to manage the building thermal energy falls under this category. It combines a modeling
step of the physical system and a control mechanism based
on the previous proposed model. Authors of [10] distinguish
between: producer agents which pilot the producer of thermal
energy such as heat pump, consumer agents which are in
charge of comfort functions, distributor agents ( subpart of
the physical distribution network) which affect the energy
transmission and which associate a set of clients to a set of
suppliers and environmental agents which provide information
relative to the state of environment.
All these types of agents are defined by a set of devices. A
device represents a real sensor, actuator or a cost. Authors
impose a hierarchical structure of the system by assuming
that a producer agent can supply only one distributor and
a consumer agent can acquire energy only from an unique
distributor. The control mechanism of this approach begins by
retrieving information from the physical system and computing
forecasted values that will be attributed to the physical system
at the next step. Then consumers develop plans of their needs,
producers develop plans of their capabilities of production
and the associated costs before sending their plans to the
corresponding distributors. Distributors connect their clients
to the appropriate suppliers offering the cheapest resources on
a specific forecast duration. At the end of the execution of
that mechanism, each agent has a refined sub-plan in which
resources are specified. No need for a central unit for piloting
plans. The fully distributed method proposed in [10] had not
been compared to a centralized or mixed multi-agent solution.
By comparing it to a not agent-based solution, Lacroix and al.
[10] remark the increase of the operating cost in return to an
increase of the thermal confort of users.
Similarly, a distributed approach is proposed in [29] to
manage small-scale electrical grid, the MAS model is composed by producers agents, consumers agents and observers
agents. The optimal collective operation of this MAS is
reached without the existence of a central supervisor and is
based on the concept of prioritization. The prioritization of
agents preferences depends on two metrics: the cost of energy
delivery and a performance measure.The performance measure
quantifies the current and past operations of the agent, it can
be in the case of a consumer agent the proportion of critical
loads provided by the smart buildings generators.
V. DISCUSSION
The physical distribution of consumers devices and microsources of the energy system and the independence of services
have oriented researchers to agent-based management solutions. We are precisely interested in one type of agent-based
management in energy systems: the anticipative management
based on agents planning.
The question is whether to adopt centralized, distributed
or semi distributed approaches for piloting the system. We
discuss here advantages and disadvantages of these classes of
management.
Starting from the centralized one, where all computations
(necessary for planning energy systems’ operation) are done
at a central unit. This management facilitates the human
interface: it is easier to send one user command to a central
unit rather than to send a user command to each agent.
Centralized management is usually more efficient in terms
of utilization of resources due the existence of central unit
owning a global picture of the system [14]: it offers interesting
solutions for conflict resolution and convergence toward a
global solution.
Another advantage of centralized approaches is their performance with tasks demanding precision, low level interactions
between agents cannot emerge the precision provided by a
central supervisor [14].
On the other side, in centralized management agents rely
strongly on a central unit, which in case of its failure can
leave the system uncontrollable [26].
This type of management is suitable for single building (small
scale energy system) where producers’ agents have same goals
[30].
Proceeding from the fact that a complex problem is solved
more quickly when it is based on locally approaches [14],
researchers were interested in fully distributed approaches. The
later have proved a high flexibility, high parallelism, robustness
and autonomy in return to high cost: this type of management
requires the use of appliances having important computation
capabilities for executing complex reasoning algorithms [8],
[14].
As solution semi distributed approaches have been imposed
offering a balance between efficiency and autonomy. The
semi distributed management can be realized by hierarchical distribution of functions: plans for next steps are first
elaborated locally and finalized at the highest level of the
hierarchy[14][17], as it can be done by giving more roles to
one agent among similar agents which have a certain degree
of autonomy.
VI. CONCLUSION
In this paper, we presented a synthesis of multi-agent
approaches which have been proposed to manage intelligent
smart systems. We distinguished three approaches: centralized
decision making for distributed execution, distributed decision
making to achieve an overall objective, distributed decision
making for distributed execution.
These multi-agent approaches provide suitable solutions for
different situations; they provide flexibility and robustness
when it is required in specific situations , they also offer high
precision and high efficiency in others cases. However, the
proposed approaches do not adapt techniques coming from
the probabilistic reasoning’s domain which seem interesting to
explore. We are working on graphically modeling the multiagent system in order to take advantage of the graphical
model’s strength in dealing with missing data.
ACKNOWLEDGMENT
We are grateful to Telnet Innovation Labs team.
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