syllabus
1. Information regarding the programme
1.1 Higher education institution 	Babeş-Bolyai University of Cluj-Napoca	
1.2 Faculty	Faculty of Mathematics and Computer Science	
1.3 Department	Departament of Computer Science	
1.4 Field of study	Computer Science	
1.5 Study cycle	Master	
1.6 Study programme / Qualification 	Modeling and Simulation 	

2. Information regarding the discipline 
2.1 Name of the discipline	Simulation Languages	
2.2 Course coordinator 	Prof.Dr. Horia F. Pop	
2.3 Seminar coordinator	Prof.Dr. Horia F. Pop	
2.4. Year of study	2	2.5 Semester	4	2.6. Type of evaluation	E	2.7 Type of discipline	Compulsory	

3. Total estimated time (hours/semester of didactic activities) 
3.1 Hours per week 	3	Of which: 3.2 course	2	3.3 seminar/laboratory	1 sem
	
3.4 Total hours in the curriculum 	42	Of which: 3.5 course	28	3.6 seminar/laboratory	14	
Time allotment:	hours	
Learning using manual, course support, bibliography, course notes	35	
Additional documentation (in libraries, on electronic platforms, field documentation) 	45	
Preparation for seminars/labs, homework, papers, portfolios and essays	47	
Tutorship	15	
Evaluations	16	
Other activities: ..................	-	
3.7 Total individual study hours 	158	
3.8 Total hours per semester	200	
3.9 Number of ECTS credits	8	

4. Prerequisites (if necessary)
4.1. curriculum		
4.2. competencies		

5. Conditions (if necessary)
5.1. for the course	•	Students will attend the course with their mobile phones shut down	
5.2.  for the seminar /lab activities	•	Students will attend the seminar with their mobile phones shut down 
•	Room with computers as needed; high level programming language environment and simulation environment	

6. Specific competencies acquired 
Professional competencies	•	understand, model and represent a CSP problem
•	understand and implement various CSP models
•	model intelligent methods as CSP problems and approach accordingly
•	understand and be acquainted with declarative frameworks
•	model machine learning problems using declarative frameworks	
Transversal competencies	•	The ability to apply computational methods in solving real world problems.
•	Responsible execution of lab assignments, research and practical reports.
•	Application of efficient and rigorous working rules.
•	Manifest responsible attitudes toward the scientific and didactic fields.
•	Respecting the professional and ethical principles.	

7. Objectives of the discipline (outcome of the acquired competencies)
7.1 General objective of the discipline
	•	To introduce the student in Constraints Programming and declarative programming frameworks	
7.2 Specific objective of the discipline
	•	To present the field of  Constraints Programming as a novel research and application domain.
•	To induce the necessity of  Constraints Programming methods and techniques by studying some relevant practical applications
•	To offer the student the instruments that will allow him/her to develop different  Constraints Programming based applications.	

8. Content
8.1 Course	Teaching methods	Remarks	
•	Part I. Introduction
Administration, introduction
Logic programming in problem solving
Graph concepts revisited	•	Interactive exposure
•	Explanation
•	Conversation
•	Didactical demonstration		
•	Part II. Fundamentals of Constraints Programming
Overview of CSP
Fundamental concepts
Problem reduction
Basic search strategies	•	Interactive exposure
•	Explanation
•	Conversation
•	Didactical demonstration		
•	Part III. Advanced topics in Constraints Programming
Search orders
Problem specific features
Stochastic search methods
Optimisation in constraints programming
Model AI methods in CSP context	•	Interactive exposure
•	Explanation
•	Conversation
•	Didactical demonstration		
Bibliography
[1] Edward P.K. Tsang, Foundations of Constraint Satisfaction, Academic Press, London and San Diego, 1993, ISBN 0-12-701610-4
[2] Roman Bartak, On-line Guide to Constraint Programming,
http://ktiml.mff.cuni.cz/~bartak/constraints/index.html
[3] Grzegorz Kondrak, A Theoretical Evaluation of Selected Backtracking Algorithms, M.Sc. Thesis, University of Alberta, Edmonton, 1994
[4] ShapeLogic, http://www.shapelogic.org	
8.2 Seminar / laboratory	Teaching methods	Remarks	
Labs 1-2 : Solve typical CSP problems in CSP/Java
	•	Interactive exposure
•	Explanation
•	Conversation		
Labs 3-4 : Implement and experiment fundamental constraints methods	•	Interactive exposure
•	Explanation
•	Conversation		
Labs 5-6 : Research report and software project	•	Interactive exposure
•	Explanation
•	Conversation		
Bibliography
[1] Edward P.K. Tsang, Foundations of Constraint Satisfaction, Academic Press, London and San Diego, 1993, ISBN 0-12-701610-4
[2] Roman Bartak, On-line Guide to Constraint Programming,
http://ktiml.mff.cuni.cz/~bartak/constraints/index.html
[3] Grzegorz Kondrak, A Theoretical Evaluation of Selected Backtracking Algorithms, M.Sc. Thesis, University of Alberta, Edmonton, 1994
[4] ShapeLogic, http://www.shapelogic.org	

9. Corroborating the content of the discipline with the expectations of the epistemic community, professional associations and representative employers within the field of the program

The content of the discipline is consistent with the similar disciplines from other romanian universities and universities from abroad, as well as with the requirements that potential employers would have in the simulations field. 	

10. Evaluation
Type of activity	10.1 Evaluation criteria	10.2 Evaluation methods	10.3 Share in the grade (%)	
10.4 Course	•	The correctness and completeness of the accumulated knowledge.	Written exam (in the regular session)	40%	
10.5 Seminar/lab activities	•	Class attendance	Grade awarded pro rata	10%	
	•	Personal projects demonstrating modeling of CSP problems and mastering of CSP methods	Evaluation of the project (software implementation,  documentation and demonstration)	20%	
	•	A personal research project demonstrating the application of CSP in a particular field or for a particular problem	Evaluation of the project (written documentation, oral presentation)	10%	
	•	A personal software project related to the research project	Evaluation of the project (software implementation,  documentation and demonstration)	20%	
10.6 Minimum performance standards	
•	Each student has to prove that (s)he acquired an acceptable level of knowledge and understanding of simulations, that (s)he is capable of stating these knowledge in a coherent form, that (s)he has the ability to establish certain connections and to use the knowledge in solving different problems.
•	Penalty points are awarded for delays in lab projects deliveryapply.
•	Successful passing of the exam is conditioned by the final grade that has to be at least 5; the written exam grade has to be at least 5. 	

Date				Signature of course coordinator 	Signature of seminar coordinator
30.09.2012		            Prof. dr. Horia F. Pop			Prof. dr. Horia F. Pop	
Date of approval  				 		          Signature of the head of department
									Prof. dr. Bazil Pârv