ARTIFICIAL INTELLIGENCE
(SYLLABUS)
University of Petrosani
Faculty: The Faculty of Mechanical and Electrical Engineering
Program Level: Undergraduate
Specialization(s): Computer Science; Control Engineering and Applied Informatics
Year of study: 3rd
Semester: II (spring semester)
Number of ETCS credits awarded: 5
Class Objectives:
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Introduction to development the theoretical and practical tools in the field of artificial
intelligence.
Assimilating knowledge about the Comprehensive knowledge on techniques, methods,
systems and networks used in the field of artificial intelligence, skills training and problemsolving skills through searching.
Assimilating knowledge about the intelligent control.
Assimilating knowledge about the searching for a solution with intelligent agents and
perceptrons.
Lectures content:
Content
The object of study of artificial intelligence. The object of
the study A.I. AI History. Research areas and applications.
Representation of knowledge. Knowledge representation
systems.
Meta-representation system. The classification system.
Organizing system.
Symbolic representation issues. Knowledge representation
using predicate logic.
Using other logical knowledge representation. Nonstandard
Logic logic. Monotonic logic and nemonotone logic.
Knowledge representation techniques through declarative
and procedural methods.
Completeness and consistency of procedural systems.
Semantic networks. Primitive relational.
The hierarchy of concepts and legacy.
Knowledge representation using frames and scripts.
Knowledge-based information systems. Knowledge
engineering.
Architecture of information systems based on knowledge.
The development of knowledge-based information systems.
Description of the knowledge base. Creating and validating
knowledge base. Implementation of knowledge-based
information systems.
Solving general problems.
Types of problems and solutions. Problem-solving methods.
Image processing and recognition methods of forms.
Recognizing theoretical-.decision.
Recognizing syntactic structure.
Fuzzy theory and superficial neural networks.
Uncertainty and imprecision. Fuzzy logic.
Number of lectures
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Characteristics of artificial neural networks. Artificial
neuron model.
Artificial neural network architectures.
Training algorithms and neural-fuzzy networks.
2
2
2
Practical class content:
Content
Safety procedures. Lab induction. Work procedures.
Getting started about symbolic processing languages LISP
data. PROLOGUE.
Arithmetic and relational LISP. The concept of atom;
instructions for the award; SET, SETD, indirect allocation
and evaluation function EVAL. List structures in LISP and
PROLOG, representation, operations on lists CAR, CDDR
Operations on lists, LIST function, APPEND, DELETE.
SUBST functions, SUBSTN, RPLACD-with examples.
Generating lists and their processing, using the taught
functions.
Implementation of functions: (a) checking if a list is empty;
(b) ordering of two numerical atoms; (c) the determination
of the length of a linear list (function + LENGTH).
Sorting lists, general notions, presentation types of sorting
existing, algorithms efficiency.(a)sorting through insertion:
Pascal. LISP, Prolog; (b) sorting through selection: LISP,
PROLOG.
Structures in LISP and Prolog: binary trees, taking in the
post-, pre- and in order (private representation structure).
LISP and PROLOG functions features to navigate the trees
according to the 3 methods.
Genetic algorithms. Applications solve the problem
committed interactive test
Initiation in Matlab and use package for neural networks.
Graphical representation of suitability functions
Implementation of associative memories using Hopfield
type network. An approximation for an applications of linear
networks with a level
Kohonen Networks. Applications to problem solving
interactive test and commit the generalized problem of
minimal shaft type invelitorii
Simulation of feedforward networks with multiple levels
Number of classes
1
2
Designing semantic networks.
Control of a robot arm with artificial neuronala network.
Final evaluation and testing for practical classes.
2
2
2
2
2
2
2
2
2
2
2
2
1
Grading Policy:
The student pass the class if obtain more than 50 points from 100 points.
Classroom Rules of Conduct:
 Turn off (or place on vibrate) cell phones and pagers during class
 Food and beverages are not permitted in classroom
 During exams, the use of cell phones, pagers, PDAs, or any other electronic devices is strictly
prohibited
Lecturer: Assoc. Prof. Ph.D. Eng. Egri Angela
e-mail: