Intelligent Agents

Објавено: October 12, 2018
  1.    Course Title Intelligent Agents
  2.    Code 3ФЕИТ07З008
  3.    Study program KTI
  4.    Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
  5.    Degree (first, second, third cycle) First cycle
  6.    Academic year/semester IV/7   7.    Number of ECTS credits 6.00
  8.    Lecturer Dr Hristijan Gjoreski
  9.    Course Prerequisites Passed: Data Structures and Algorithm Analysis

10.    Course Goals (acquired competencies):  Introduction to reasoning and knowledge representation with intelligent agents. Working with Prolog and first-order logic. Upon completion, the student will be able to write algorithms for intelligent agents for a specific purpose.

11.    Course Syllabus: Introduction to Intelligent Systems. History. What is an intelligent system. Reasoning. Agents. Search algorithms. Markov decision making process. Games. Minimax. Constraints. Objects representation. First-order logic. Prolog. Problem solving with state space search. Decision trees. Neural networks. Error propagation. Bayesian networks. Advanced propagation. Genetic algorithms. Learning in neural networks. Recurrent neural networks. Techniques for Deep Learning. Application of NN and DL.
12.    Learning methods:  Lectures, auditory and laboratory exercises
13.    Total number of course hours 2 + 2 + 1 + 0
14.    Distribution of course hours 180
15.    Forms of teaching 15.1. Lectures-theoretical teaching 30
15.2. Exercises (laboratory, practice classes), seminars, teamwork 45
16.    Other course activities 16.1. Projects, seminar papers 25
16.2. Individual tasks 20
16.3. Homework and self-learning 60
17.    Grading 17.1. Exams 10
17.2. Seminar work/project (presentation: written and oral) 10
17.3. Activity and participation 0
17.4. Final exam 80
18.    Grading criteria (points) up to 50 points     5 (five) (F)
from 51 to 60 points     6 (six) (E)
from 61 to 70 points     7 (seven) (D)
from 71 to 80 points     8 (eight) (C)
from 81 to 90 points     9 (nine) (B)
from 91 to 100 points   10 (ten) (A)
19.    Conditions for acquiring teacher’s signature and for taking final exam Laboratory exercises
20.  Forms of assessment  Two partial exams during the semester lasting 120 minutes each or one final written exam in an appropriate exam session lasting 120 minutes. Evaluation of laboratory exercises. Possibility to do a seminar project that will be evaluated
21.  Language Macedonian and English
22.    Method of monitoring of teaching quality Internal evaluation and questionnaires
23.    Literature
23.1. Required Literature
No. Author Title Publisher Year
1 Stuart Russel Artificial Intelligence: A Modern Approach (3rd Edition) Pearson 2009
2 Lin Padgham, Michael Winikoff Developing Intelligent Agent Systems: A Practical Guide Wiley 2004
3 Prateek Joshi Artificial Intelligence with Python Packt Publishing 2015