Agent Oriented Patient Scheduling System: A Concurrent Metatem Based Approach

Hossainara Begum Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 8, (Part - 5) August 2015, pp.133-140
www.ijera.com 133 | P a g e
Agent Oriented Patient Scheduling System: A Concurrent
Metatem Based Approach
Hossainara Begum1
, Shibakali Gupta2
1
Dept. of Computer Science & Engineering, University Institute of Technology, Burdwan, West Bengal, India
Abstract
The Problem of Patient Scheduling[6] is a major issue in a Medical Healthcare System[4]. In India, Healthcare
is an 80 billion dollar Industry and is growing at an average rate of 17% annually. However, quality healthcare
is still out of reach for many. Each year thousands of fatalities arise simply due to the fact that patient could not
be provided with proper medical facilities at the right time. A software agent may be a member of a Multi-Agent
System[2][5] (MAS) which is collectively performing a range of complex and intelligent tasks. Using
Concurrent Metatem[3], a Multi-Agent Language, we have attempted to model a patient scheduling
system[4][6] that can help hospitals collaborate among them through a Liaison-Agent, in order to provide
patients with the best care possible. Patients should no longer have to be turned down when hospitals are packed
to capacity; instead, they could simply be shifted to another hospital. Hospitals and even doctors are assigned to
patients after an automated process of matching patient needs with doctor expertise and hospital infrastructure-
leading to reduced waiting-time while maximizing efficiency and potentially saving lives.
Index Terms—Patient Scheduling, Multi Agent System, Concurrent Metatem
I. INTRODUCTION
The Problem of Patient Scheduling[6] is a major
issue in a Medical system[10] and its characterized
by high certainty and dynamic changes in patient
treatment. This scheduling is very complex and
concurrency may occur if more than one patient is
needed to be scheduling in a same time. If possible
then sometimes they are suggested to be transfer with
proper information. Patients are to be scheduled to
reduce the waiting time and to reduce idle time of
resource[1]. This scheduling problem can be solved
for multiple departments in Medical System[6].
Clinical/nonclinical department interaction makes
inappropriate patient transfer. Clinical/clinical
department interaction increases the waiting time of
the patient in a department. In health department,
patients can undergo any checkup. When a particular
resource gets overcrowded the patients can go any
free resources because there is no constraint. New
method has been proposed in this paper to transfer
the patients from overcrowded resources to free
resources. To optimize its goal we have used a Multi
Agent based patient scheduling technique[4][6].
Multi agent system[2] can be used to solve this
problem which is difficult or impossible for an
individual agent or a monolithic system to solve.
Multiple agents share the messages to interact with
each other. Multi Agent System[2] change the
complex problem into simple which can be solved
easily. Multi Agent System[2] is a system composed
of multiple interacting intelligent agents[2], is used to
solve many problems that are difficult or impossible
for an individual agent to do. In medical[10] expert
system they simplify complex problem solving by
dividing the necessary knowledge into few subunits
to which an independent intelligent agent[2] is
associated and thus the agents’ activity will be so
coordinated. In this way we can refer Multi Agent
System[2]. A Concurrent Metatem[3] system
contains a set of concurrently executing agents which
can communicate with each other via asynchronous
broadcast message passing. Here we have tried to
implement a Multi Agent Based Patient
Scheduling[4][6] by using Concurrent Metatem[3]
approach. A Multi Agent System[2] is a computer
based system, used to solve several kinds of problem
that are quite difficult and impossible for an
individual to solve. It is composed of multi
interacting intelligent agents[2] within an
environment. Multi-Agent Systems[2] consist of
several agents and their several environments. Multi
Agent Systems research refers to software agents [2].
The agents in a Multi Agent System[2] could equally
to be robots, humans or human teams. A Multi-Agent
System[2] may contain combined of human-agent.
The medical expert system[10] agents can perceive
from environment and interact with the environment
by learning and executing different action on the
environment autonomously. They can communicate
with each other that allow co-operative problem
solving[1]. Multi Agent System[2] can be applied to
Artificial Intelligence[2] [9] which divide all the
necessary knowledge or information into subunits to
which an independent intelligent agent[2] [10] is
associated to act. They can solve problem by
coordinating the agents’ activity. The aim of this
RESEARCH ARTICLE OPEN ACCESS

system is to recognize how important processes can
be coordinated. In Multi Agent System[2] an agent
[12] is a computerized entity like a computer
programme or a robot. An agent can interact
autonomously because it has the capacity to adopt
with environment changes. A Multi Agent System[2]
is composed of a set of computer processes exist at
the same time, communicate with each other in an
environment.
II. PATIENT SCHEDULING
COORDINATION
ARCHITECTURE
Patient scheduling in Multi agent System[2][6]
process is developed for dynamic load balancing in
agent based simulation. Each agent executes their
action by message communication with others. Based
on this scheduling method, we have tried to schedule
all the patients (concurrently) by using Multi Agent
System[2]. This method provide patient the best care
possible. This method reduces the waiting time of the
patient and improves the patient satisfaction. For
better treatment Patients should be shifted to another
resources. We have designed the architecture of
patient scheduling, is shown below:
Fig. 1.Architecture of the proposed patient
scheduling
From the above architecture, there are three
types of Agents are used:
1. Hospital Agent (HA): Acts on behalf of a
Hospital. Each hospital has a Hospital Agent
which is responsible for Patient Admission.
2. Patient Agent (PA): Acts on behalf of a Hospital.
Each hospital has a Patient Agent which handles
multiple patients and allots rooms and doctors to
them.
3. Hospital Liaison Agent (HLA): Central Agent
which is responsible for hospital communication
and patient shifts between them.
Fig. 2.Architecture of the Agent communication
Hospital Liaison Agent (HLA) is in charge of
agent communication. It can receive
Resource/Hospital agent information, process the
patient agent. It is a type of Central Agent and also
maintains the count of patients to be shifted. The
Patient Agent can handles multiple patient and
provide proper medical care based on their
requirements. At each resource/Hospital there is one
Patient Agent which is in charge of patient
scheduling procedure[1], selecting the Patient sets
based on some criteria and moving them.
Resource/Hospital Agents send and receive the
requests from Hospital Liaison Agent. When a
particular resource/Hospital gets overcrowded or if
Patients require better treatment to be alive, it needs
to migrate the exceeding number of patients to
another resource in order to reduce the waiting time
of the patient and to improve the resource utilization
for Patient Satisfaction. The overcrowded resource
will send request to neighborhood resource via
Hospital Liaison Agent which response if it has
enough space to admit that patient. Moreover, rooms
are allotted and available doctors are assigned to
them by finding their needs through an automated
process. And If not then it will ignore the request[1].
III. THE PROPOSED METHOD OF
PATIENT SCHEDULING
A. Step1: Procedure Patient Admission-
1. Input Patient Details like name, sex, age, history,
department, status and criteria.
2. Check if there is any vacancy in the Current-
Hospital for the given department and criteria.
3. If there is no vacancy, go to step 4 else go to step
5.
4. Attempt to transfer the patient to another
hospital, and go to step 6.
5. Allot hospital resources to the concerned patient.

6. Stop.
B. Step2: Procedure GetVacancyStatus-
1. Input Hospital-Name, Patient-Department and
Patient-Criteria.
2. Check the number of vacant rooms in the given
department at the given hospital.
3. If the number of vacancies is 0, then return
FALSE.
4. If the number of vacancies is more than 0, check
whether the patient's criteria and the hospital's
facilities match or not.
5. If they do match, return TRUE, else return
FALSE.
6. Stop.
C. Step3: Procedure GetVacancyCount-
1. Input Hospital-Name and Patient-Department.
2. Initialize C to 0.
3. For Each Room in the given Hospital, do step 4.
4. If the selected room is not booked AND the
selected room belongs to the given department,
then increment C by 1.
5. Return C.
6. Stop.
D. Step4: Procedure CriteriaMatch-
1. Input Hospital-Name, Criteria and Patient-
Department
2. If the given hospital does not have the given
department, then return FALSE.
For each specific_hospital_criteria and
3. For each Specific_Hospital_Criteria and
Specific_Patient_Criteria In Hospital_Facilities
and Patient_Criteria, do steps 4-5.
4. Compute P=ABS(Specific_Hospital_Criteria -
Specific_Patient_Criteria).
5. If P > CRITERION_THRESHOLD (for that
specific criteria) then return FALSE.
6. Return TRUE.
7. Stop.
E. Step5: Procedure ShiftPatient-
1. Input Patient-Name, Sex, Age, Patient-History,
Patient-Criteria, Patient-Department.
2. Initialize Proposed_Hospitals to NULL.
3. For each Hospital IN the list of all registered
hospitals, do step 4.
4. If vacancy status at the selected hospital with
respect to the patient-criteria is TRUE, then add
the selected hospital to the set of proposed
hospitals.
5. If Proposed_Hospitals is NULL, then return
FALSE.
6. Initialize BestHospital to NULL and
DepartmentRank to 0.
7. For each Hospital in Proposed_Hospitals do
steps 8-9.
8. Set R = Department Rank of the selected hospital
and given patient-department.
9. If R > DepartmentRank, set DepartmentRank to
R and BestHospital to Selected Hospital.
10. Allocate resources to the patient in the
BestHospital.
11. Return TRUE.
12. Stop.
F. Step6: Procedure AllotResourceToPatient-
1. Input Name, Sex, Age, Patient-History, Criteria,
Patient-Status, Department.
2. Allot new patient ID to the patient.
3. Allot new room to the patient.
4. Allot new doctor to the patient.
5. Record ID, Room, Doctor, Patient-Details onto
database.
6. Stop.
G. Step7: Procedure AllotRoom-
1. Input Age, EmergencyLevel, Status, Department.
2. If Status=Discharged then return NULL.
3. If Status=Surgery then return the first available
OT.
4. If Department=Unallocated then go to step 5 else
go to step 11.
5. If Status=General_Admission then return a
vacant Room in the "General" department.
6. If Status=Emergency or Status=In_Recovery
then go to step 7.
7. If Age<=1 then return a vacant Room in the
"NICU" department.
8. If Age>1 And Age<=10 then return a vacant
Room in the "PICU" department.
9. If Age>10 And EmergencyLevel>=9 return a
vacant Room in the "CCU" department.
10. If Age>10 And EmergencyLevel>=7 And
EmergencyLevel<9 then return a vacant Room in
the "ICU" department.
11. Return a vacant Room in the given department.
12. Stop.
H. Step8: Procedure AllotDoctor-
1. Input EmergencyLevel, Status, Department.
2. Initialize BestMatch=0, BestDoctor=NULL.
3. Initialize AvailableDoctors = Set of doctors in
the given department And who are available on-
call.
4. For each Doctor in AvailableDoctors do steps 5-
6.
5. Set MatchScore=DoctorPatientScore(Age,
Emergency, Status, Doctor).
6. If MatchScore>BestMatch then set
BestMatch=MatchScore and
BestDoctor=Doctor.
7. Return BestDoctor.
8. Stop.

I. Step9: Procedure GetDoctorPatientScore-
1. Input Age, EmergencyLevel, Status, Doctor.
2. Retrieve from database the Specialization,
EmergencyExpertise and AgeSpecialization of
the given doctor.
3. If AgeSpecialization=Paediatric and Age>=18
then return 0.
4. Initialize Score=10*(EmergencyExpertise -
EmergencyLevel).
5. If Specialization=Surgery then go to step 6 else
go to step 7.
6. If Status=Surgery then Increment score by 10
else Decrement score by 10.
7. If Specialization=Diagnosis then go to step 8
else go to step 9.
8. If Status=Diagnosis then Increment score by 10
else Decrement score by 10.
9. Increment score by 10.
10. Return score.
11. Stop.
IV. PROPOSED ALGORITHM
PATIENT/HOSPITALCRITERIA(for EACH
criteria, one CRITERION THRESHOLD will be
taken):
1. Finance (Value Range: 0-1)
2. Emergency (Value Range: 0-1)
Algorithm:-
J. Step1: Procedure PatientAdmission-
Patient Admission (Name, Sex, Age, Patient
History, Criteria, Patient Status, Patient Dept) begin
Has Vacancy=Get Vacancy Status(this Hospital,
Criteria, Patient Dept);
if (has Vacancy=false) then
RequestPatientShift(Name,Sex,Age,Patient
History,Criteria,PatientStatus,PatientDept);
return;
End if
AllotResourceToPatient(Name,Sex,Age,Pati
entHistory, Criteria, PatientStatus,
PatientDept);
End
K. Step2: Procedure GetVacancyStatus-
GetVacancyStatus(Hospital,PatientCriteria,PatientDe
pt) begin
X=GetVacancyCount(Hospital,PatientDept);
if(X=0)
then return 0;
if(CriteriaMatch(Hospital,PatientCriteria,PatientDept
)=true) then return true; return false;
End
L. Step3: Procedure GetVacancyCount-
GetVacancyCount(Hospital,Dept)
begin
C=0;
for each Room in Hospital do begin
if(Room.Booked=false And
Room.Deptartment=Dept) then C=C+1
end if
end
return C;
end
M. Step4: Procedure CriteriaMatch-
CriteriaMatch(Hospital,Criteria2,PatientDept) begin
if (Hospital.hasDepartment(PatientDept)=false) then
return false;
for each (X,Y) in (Criteria1,Criteria2) do begin
if (ABS(X-Y)> CRITERION_THRESHOLD) then
return false;
end
return true;
end;
N. Step5: Procedure ShiftPatient-
ShiftPatient(Name,Sex,Age,PatientHistory,Criteria,P
atientDept) begin
ProposedHospitals=NULL;
for each Hospital in AllHospitals do begin
if(GetVacancyStatus(Hospital,Criteria)=true) then
ProposedHospitals=ProposedHospitals 
Hospital;
end if
end
if(ProposedHospitals=NULL) then return false;
bestHospital=NULL,deptRank=0;
for each Hospital in ProposedHospitals do begin
R=getDepartmentRank(Hospital,PatientDept);
If(R>deptRank) then
deptRank=R;
bestHospital=Hospital;
end if
end
bestHospital.AllotResourceToPatient(Name,Sex,Age,
PatientHistory,Criteria,PatientStatus,PatientDept);
return true
end
O. Step6: Procedure AllotResourceToPatient-
AllotResourceToPatient(Name,Sex,Age,PatientHistor
y,Criteria,PatientStatus,PatientDept) begin
Room=AllotRoom(Age,Criteria,Emergency,PatientSt
atus,PatientDept);
Doctor=AllotDoctor(Age,Criteria,Emergency,Patient
Status,PatientDept);
record Patient, Room, Doctor, Name, Sex, Age,
PatientHistory, Criteria, PatientStatus;
end;
P. Step7: Procedure AllotRoom-
AllotRoom(Age,Emergency,Status,Department)
begin

if(Status=DISCHARGED) then return NULL;
if(Status=SURGERY) then return getFreeOT();
if(Department=UNKnown)then
if(Status=EMERGENCY OR
Status=RECOVERY) then if(Age is NE)then
return Room where Room.Booked=false
&& Room .Dept=”NICU”;
else if(Age IS CHILD) then
return Room where Room.Booked=false
&& Room.Dept=”PICU”; else
if(Emergency is VERY_HIGH) then
return Room where
Room.Booked=false &&
Room.Dept=”CCU”;else
return Room where
Room.Booked=false &&
Room.Dept=”ICU”; end if
end if
else if(Status=GENERAL_ADMISSION)then
return Room where
Room.Booked=false&&
Room.Dept=”GENERAL”;
end if
else
if(Status=UNDER_SPECIALIZED_TREATMEN
T) then
Return Room where Room.Booked=false
&& Room.Dept=Department;
end if
end if
end;
Q. Step8: Procedure AllotDoctor-
AllotDoctor(Emergency, Status, Department)
begin
bestMatch=0, bestDoctor=NULL;
AvailableDoctors=get doctors from database where
Dept=Department;
for each Doctor in AvailableDoctors do begin
matchScore=GetDoctorPatientScore(Age,Emergen
cy,Statu s,Doctor);
if(matchScore>bestMatch) then
bestMatch=matchScore;
bestDoctor=Doctor;
end if;
end;
return bestDoctor;
end;
R. Step9: Procedure GetDoctorPatientScore-
GetDoctorPatientScore(Age, Emergency, Status,
Doctor)
begin
Specialization, EmergencyExpertise,
AgeSpecialization = get record of Doctor;
if(AgeSpecialization = Pediatric AND Age > 18)
return 0;
score=10*(EmergencyExpertise-Emergency);
if(Specialization = Surgery) then
if(Status=4) then
score=score+10;
else
score=score-10;
end if
else if(Specialization = Diagnosis) then
if(Status=2) then
score=score+10;
else
score=score-10;
end if
else
score=score+10;
end if
return score;
end;
V. TRANSACTIONAL ANALYSIS
THROUGH TIME SEQUENCE
DIAGRAM
Time Sequence Diagram provides the way of
concurrent process. Hospital Liaison Agent can
establish a connection among many resources. when
a particular resource agent gets overcrowded then
that will send request to another neighborhood
resource agent via Hospital Liaison Agent. It will
check whether it has enough capacity to do treatment
for emergency patients and also check whether rooms
are available or not, the required doctors are there or
not. If all those criteria become matched it will
accept. If not then it will reject the request from
overcrowded resource agent. Patients are shifted
based on some criteria. Patients are transferring from
overcrowded resource agent to neighborhood
resource agent. Patients will be moved to another
resource agent. Then migrated agent will be removed
from overcrowded resource agent. This process will
be repeated until the entire overcrowded patient is
scheduled. This multi agent based Patient
Scheduling[4][6] method has been used to search for
optimized scheduling scheme[5], including migrated
agent sets. Only a single Hospital Liaison Agent
(HLA) exits in the entire system, responsible for Inter
Hospital Communication. In this Diagram this
selected Hospital Agent simulates the behavior of all
Hospital Agent in the System. Messages sent to this
Agent are intact symbolic of sending the message to
every Hospital Agent. Only the Admit Patient
message is sent to a single Hospital Agent which was
selected by the Hospital Liaison Agent (HLA) for
transferring the Patient. Thus, the selected Patient
Agent refers to the Patient Agent of the hospital
which was selected by the Hospital Liaison Agent
(HLA) for transferring the Patient[1].

Fig. 3.Time Sequence Diagram
Above diagram describes the time sequence
diagram of a complete patient scheduling processing
medical system. One patient is to be admitted. Here,
T1- patient Approach for admission to this Hospital
Agent.
T2- hospital Agent asks to Patient Agent for Resource
Allotment of that Patient.
T3- Patient ID provided/allotted by Patient Agent.
T4- Room provided/allotted by Patient Agent.
T5- Doctor provided/allotted by Patient Agent.
T6- this Hospital Agent request to Hospital Liaison
Agent for Patient Shifting.
T7- Hospital Liaison Agent request for Vacancy
report to other Hospital Agent
T8- Hospital agent search to get Vacancy status.
T9- Hospital agent search criteria of patient.
T10- Hospital agent provides a vacancy report to the
Hospital Liaison Agent.
T11- based on the vacancy report, Patient are
requested to admit.
T12- Hospital Agent request to Patient Agent for
resource allotment of that Patient.
T13- Patient Agent of the Selected Hospital generates
a Patient ID.
T14- Patient Agent of the Selected Hospital
Provide/allot room.
T15- Patient Agent of the Selected Hospital
provide/allot Doctor.
VI. CONCURRENT METATEM
Concurrent MetateM[2][3] provides an
operational framework[9] through which societies of
MetateM[7][8][9] processes can operate and
communicate with each others. the notion of
executing a logical specification which generates
individual agent behavior[3]. The temporal
connectives [7][8][9] can be divided into two
categories which are as follows:
1. Some Strict past time connectives: ●
(weak last), (Strong Last), ◊ (was), ■ (heretofore),
 (Since) and Z (Zince or weak since)[3][7][8][9].
2. Some Present and future time
connectives: O (next), ◊ (sometime), □ (always), Ʋ
(until) and ⱳ (unless).
Here whenever one job is executed the antecedent
part will always be about past time and the
consequent part will always be about present and
future time[3][7][8][9].
For Examples-
1. □important (agents) means “it will always be
true that agents are important”[3][7][8][9].
2. ◊ Important (Process) means “sometime in the
future, Process will be important”[3][7][8][9].
3. ♦ Important (Prolog) means “sometime in the
past it was true that Prolog was
important”[3][7][8][9].
4. (¬friends (us)) Ʋ apologize (you) means “we are
not friends until you apologize”[3][7][8][9].
5. Ο apologize (you) means “tomorrow (in the next
state), you apologize”[3][7][8][9].
VII. IMPLEMENTATION USING
CONCURRENT METATEM
APPROCH
A. Step1:
Patient-agent (allotResourceForPatient)
[patientAdmitted]:
Start => waiting
True => ◊ waiting
allotResourceForPatient(Name,Sex,Age,History,Cr
iteria,Status,Dept)=>allotRoom(HospitalName,Dep
t,Criteria,Status,Age)
allotRoom(Hospital,Name,Dept,Criteria,Status,Age
)=>¬ waiting
allotRoom(Hospital,Name,Dept,Criteria,Status,Age
)=>allotDoctor(HospitaName,Dept,Criteria,Status
,Age)
allotDoctor(Hospital,Name,Dept,Criteria,Status,Ag
e)˄doctor(Hospital,X,Dept,Criteria) =>
PatientAdmitted(Name,X)
allotDoctor(Hospital,Name,Dept,Criteria,Status,Ag
e)=>waiting.
B. Step2:
hospital-liaison-agent(shiftPatient,vacancyReport)
[hasVacancy,admitPatient]:
start => known(hospital-agent)
shiftPatient(Name,Sex,Age,History,Criteria,Status,
Dept)˄known(H)˄hospital(H,C,Criteria)=> ¬
waiting
shiftPatient(Name,Sex,Age,History,Criteria,Status,
Dept)˄known(H)˄hospital(H,C,Criteria)=>hasVaca
ncy(Name, Sex,Age,History, Criteria,Status, Dept)
vacancyReport(N)˄(0<N)˄¬waiting=>admitPatie

nt(Name Sex, age, History, criteria Status,Dept)
vacancyReport(N) ˄ (0<N) ˄ ¬waiting
=>waiting.
C. Step3:
hospitalagent(admitPatient,patientAdmitted,disc
hargePatient,hasVacancy)
[shiftPatient,vacancyReport]:
admitPatient(Name,Sex,Age,History,Criteria,Status
,Dept)˄vacancy(P)˄(0<P)=>servePatient(Name,S
ex,Age,History,Criteria,Status,Dept)
admitPatient(Name,Sex,Age,History,Criteria,Status
,Dept)˄vacancy(0)=>ShiftPatient(Name,Sex,Age,
History,Criteria,Status,Dept)
servePatient(Name,Sex,Age,History,Criteria,Status,
Dept)˄vacancy(X)˄ (Y=X-1)=>vacancy(Y)
servePatient(Name,Sex,Age,History,Criteria,Status,
Dept)=>allocateResourceForPatient(Name,Sex,Age
,History,Criteria,Status,Dept)
dischargePatient(Name,Sex,Age,History,Criteria,St
atus,Dept)=> vacancy(X)˄ (Y=X+1) =>
vacancy(Y)
hasVacancy(Name,Sex,Age,History,Criteria,Status,
Dept)˄department(self,Dept)˄vacancy(X)˄(0<X)=>
vacancyReport(1)
Dept)˄vacancy(0) =>vacancyReport(0)
Dept)˄department(selfDept) => vacancyReport(0).
VIII. EXPERIMENTAL RESULTS
To implement this scheduling method for
distributed patients with grouping, hardware
requirements and software requirements are used.
Hardware Requirements are Intel® Core (TM) i3-
4030U CPU @ 1.90 GHz processor and 64- bit
operating system are used. Software Requirements
are JDK 1.6 java software used with METATEM
package. Three resources and Ten patient Agents are
created to do the scheduling. Initially create the
patient agent by retrieving from database. Each
patient agent have unique agent id which will be
generated while creating an agent. Created patient
agent should be send to resource agent to do their
treatment. Through agent communication one
resource agent sends the message to another resource
agent. After receiving the responses, patient agents
are moved from one resource agent to other resource
agent. Available rooms are allotted and Available
doctors are also be assigned to them. Thus the
scheduling has been done and minimizes the fatalities
of a patient lives.
IX. CONCLUSION
In this paper we have framed a novel Multi
Agent based patient scheduling[6] algorithm and we
have implemented it through MetateM language. In
this algorithm we have tried to execute all the
processes in a concurrent way. And to hold the
rhythm we introduced Concurrent MetateM[2][3]
approach. But in future we can use any other novel
(customized) techniques for better concurrency.
Color Petri Net (CPN) concept can be an alternative
in this context.
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[9.] Marco Alberti, Faculdade de Ciências e
Tecnologia, Universidade Nova de Lisboa,
"Agent-Oriented Programming".
[10.] Antonio Moreno, Computer Science &
Mathematics Department, Universitat
Rovira i Virgili, ETSE. Campus Sescelades.
Av. dels Països Catalans, 26, 43007-
Tarragona, Spain, amoreno@etse.urv.es,
"Medical Applications of Multi Agent
Systems.

Agent Oriented Patient Scheduling System: A Concurrent Metatem Based Approach

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