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
Semester
Hours: C+S+L
Category
Type
Computer Science - in Romanian
6
2+0+2
speciality
optional
Mathematics-Computer Science - in Romanian
6
2+0+2
speciality
optional
Information engineering - in Romanian
8
2+0+2
optional
Teaching Staff in Charge
Assoc.Prof. CZIBULA Gabriela, Ph.D.,  gabiscs.ubbcluj.ro
Aims
1. To present the most important intlligent 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
References
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.
Links: Syllabus for all subjects
Romanian version for this subject
Rtf format for this subject