Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Graduate
SUBJECT

Code	Subject	
MII0010	Development Methods for Intelligent Systems	

Section	Sem.	Hours: C+S+L	Category	Type	
Computer Science - in Romanian	6	2+0+1	speciality	optional	
Information engineering - in Romanian	8	2+0+2	speciality	optional	

Teaching Staff in Charge	Assoc.Prof. CZIBULA Gabriela, Ph.D., gabis@cs.ubbcluj.ro	
Aims	1. To present the most important intelligent activities: searching, games, planning, learning.
2. To present the field Intelligent Agents as a new research and development area in the field of Artificial Intelligence.
3. To present the main aspects related to the design and implementation of Intelligent Agents and how are they related to other programming paradigm (especially object oriented programming).
	
Content	1. Intelligent Systems 
1.1. Artificial Intelligence - main aspects
1.2. Intelligent Agents
1.2.1. The concept of intelligent agent
1.2.2. The structure of intelligent agents
1.2.3. Taxonomies
1.2.4. Abstract architectures for intelligent agents
1.2.5. Concrete architectures for intelligent agents
1.2.6. Programming languages for agents
1.2.7. Intelligent agents and objects

2. Searching
2.1 Uniformed search
2.2 Constraint Satisfaction Problems
2.3 Path Finding Problems
2.3.1 Informed search (heuristics)

3. Game playing
3.1. Introduction
3.2. The Minimax procedure
3.3. Alpha-beta pruning
3.4. Refinements: waiting for quiescence, secondary searching, using an archive of moves, alternatives to Minimax
3.5. Iterative deepening

4. Planning
4.1. Introduction
4.2. Blocks world
4.3. Components of a planning system
4.4. Planning using stack of goals
4.5. Nonlinear planning using constraints posting
4.6. Hierarchical planning
4.7. Reactive systems
4.8. Other planning techniques

5. Learning
5.1. The general model of a learning agent
5.2. Learning a domain
5.3. Learning strategies
5.4. Types of learning: supervised, unsupervised. Examples

6. Mathematical models for learning agents
6.1. Markov Decision Processes
6.2. Partial Observable Markov Decision Processes
6.3. Hidden Markov Models

7. Knowledge representation
7.1. Representation methods
7.2. Properties
7.3. The frame problem
	
Bibliography	1. SERBAN, G., Sisteme inteligente. Instruire automata, Ed. Risoprint, Cluj-Napoca, 2008
2. SERBAN, G., POP, HORIA F.:Tehnici de Inteligenta Artificiala. Abordari bazate pe Agenti Inteligenti, Ed. Mediamira, Cluj-Napoca, 2004. 
3. POP, HORIA  F. - SERBAN, GABRIELA: Inteligenta Artificiala. Cluj-Napoca: Centrul de Formare Continua si Invatamant la Distanta, 2003. 
4. RUSSEL, J.S, NORVIG, P., Artificial Intelligence- A Modern Approach, Prentice- Hall, Inc.,New Jersey, 1995
5. WINSTON, P. H.: Artificial Intelligence. Addison Wesley, Reading, MA, 1984, 2nd ed.
6. SUTTON, RICHARD S. - BARTO, ANDREW G.:  Reinforcement learning. London : The MIT Press Cambridge, Massachusetts, 1998.  
7. HARMON, M. -  HARMON, S.: Reinforcement Learning - A Tutorial. Wright State University, 2000. [www-anw.cs.umass.edu./~mharmon/rltutorial/frames. html]

	
Assessment	The final grade will be computed taking into account the following: lab activity (NA - 20%), a project realised during the semester(NP -  40%), the written exam (NE - 40%).
The access to the written paper is conditioned by the grade NP, which has to be at least 5. In order to promote the final exam, the final grade has to be at least 5. More about the evaluation can be found at http://www.cs.ubbcluj.ro/~gabis/cerinte/Req_TRSI.htm.