Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: Informatica
Programul de studii: Sisteme Inteligente - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MII1001	Cooperative Intelligent Agents	

Semester	Hours: C+S+L	Category	Status	
3	2+1+0	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
CZIBULA Gabriela	Ph.D.	Prof.	Programming  Languages and Methods	*	*		

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
To introduce the student a new field of Artificial Intelligence - Distributed AI. 
To allow a comparative approach of theoretical aspects in distributed and classic AI. 
To present the field of Intelligent Agents as a new research and application domain of Artificial Intelligence. 
To induce the necessity of DAI through the study of relevant industrial and practical applications. 
To present some “elementary” intelligent activities and how are they achieved by cooperative agents in multiagent systems.

IV. Course contents
1. Introduction. 
2. Agents and Intelligent Agents. 
3. Multiagent Systems and societies of agents. 
4. Practical applications of (intelligent) agents and multiagent systems.
5. Distributed Constraint Satisfaction.
6. Distributed Path Finding.
7. Game playing. 
8. Learning in multiagent systems. 
9. Practical applications of Distributed Artificial Intelligence.

V. Bibliography
1.	Şerban, G., Sisteme multiagent în Inteligenţa Artificială Distribuită. Arhitecturi şi aplicaţii. Editura RisoPrint, Cluj-Napoca, 2006 
2.	Şerban, G., Pop, H.F., Tehnici de Inteligenţă Artificială. Abordări bazate pe Agenţi Inteligenţi, Ed. Mediamira, Cluj-Napoca, 2004 
3.	Weiss, G. (Ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999 
4.	Russell, J.S, Norvig, P., Artificial Intelligence- A Modern Approach, Prentice- Hall, Inc., New Jersey, 1995	

VI. Thematic of didactic activities per weeks
1.	Introduction  [1, ch. 1] (1 lecture – S1 + 1 seminar – S1)
•	Distributed Artificial Intelligence
•	The concept of agent and intelligent agent
2.	Intelligent Agents [1, ch. 2-3] (2 lectures – S2, S3 + 2 seminars – S2, S3)
•	Definitions, properties, taxonomies
•	Abstract and concrete architectures for intelligent agents
•	Software agents
•	Mobile agents, interface agents
•	Application domains
•	Agents and Objects
•	Agents and Expert Systems
•	Agent based software engineering
3.	Multiagent systems and societies of agents [1, ch. 4] (1 lecture – S4 + 1 seminar – S4)
•	Coordination, cooperation, communication - protocols
•	Negotiation
•	Communication languages between agents
•	KQML, FIPA-ACL
4.	Applications of agents and multiagent systems [1, ch. 9] (2 lectures – S5, S6 + 2 seminars – S5, S6)
•	Intelligent agents in e-business and e-commerce
•	Intelligent agents in e-banking
•	Intelligent agents for Distributed Data Mining
•	Information agents
•	Intelligent agents in software engineering 
•	Ant systems
5.	Distributed constraint satisfaction problems [1, ch. 5] (2 lectures – S7, S8 + 2 seminars – S7, S8)
•	The problem
•	The hyperresolution besed consistency algorithm
•	Asynchronous backtracking
6.	Distributed path finding problems [1, ch. 5] (2 lectures – S9, S10 + 2 seminars – S9, S10)
•	Asynchronous dynamic programming
•	Learning Real Time A*
•	Bidirectional search algorithm
•	Real time multiagent search algorithm 
7.	Two player games [1, ch. 5] (1 lecture – S11 + 1 seminar – S11)
•	The problem
•	The Minimax search procedure
•	Minimax with alpha-beta pruning
8.	Learning in multiagent systems [1, ch. 7] (2 lectures – S12, S13 + 1 seminar – S12)
•	Types of learning
•	Cooperative learning in multiagent systems
•	Team learning
•	Concurrent learning
•	Application domains for multiagent learning
9.	Practical applications of Distributed Artificial Intelligence [1, ch. 9] (1 lecture – S14)
•	Where in the products life cycle can be used agents
•	The advantages of using agents
•	Methodologies for developing agent based applications

Seminars S13 and S14 – presentation of semester projects	

VII. Didactic methods used
Lectures, conversations, projects, exercises, individual study, homework assignments.	

VIII. Assessment
The activity ends with a written final exam (grade E). During the semester, the students will have to prepare a theoretical report (grade R) and a practical project that will have to illustrate the importance of using intelligent agents on a specific task (grade P). The ab activity will also be graded (grade L). The students activity during the semester will be also considered (grade A).  The final grade is the weighted mean of the five grades mentioned above. The final grade = 40%E + 20%R + 15%L + 15%P + 10%A. Students who demonstrate excellent research performance by developing the project to publication will get an extra score of 10% from the final grade. Successful passing of the exam is conditioned by the final grade that has to be at least 5. More about the evaluation can be found at http://www.cs.ubbcluj.ro/~gabis/cerinte/Req_CIA.htm.	

IX. Additional references
1.	Sutton, R.S., Barto, A.G., Reinforcement learning, The MIT Press Cambridge, Massachusetts, London, England, 1998 
2.	Wooldrige, M.: Agent-Based Software Engineering. London: Mitsubishi Electric Digital Library Group, 1997  
3.	Shoham, Y.: Agent-oriented programming. Artificial Intelligence. 60(1), 1993, pp.51-92.
4.	Internet resources	

	Dean,							Chair head,
            
	Date,							Course responsible,
	1.10.2010						Prof. CZIBULA Gabriela, Ph.D.