Graduate study programme

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Intelligent Systems DRab2-02

ECTS 7 | P 45 | A 15 | L 15 | K 0 | ISVU 149743 | Academic year: 2019./2020.

Course groups

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Course lecturers

BLAŽEVIĆ DAMIR, Lecturer
KÖHLER MIRKO, Associate
SUŠAC FILIP, Associate

Goals

Present skills in the area of intelligent systems. Introduce required intelligent agent features for problem solving. Develop problem state space. Describe problem solving in first order logic. Introduce students with knowledge representation, planning and decision making procedures in environment with or without uncertainty.

Conditions for enrollment

Requirements met for enrolling in the study programme

Course description

Intelligent agents. Problems and their search spaces. Types of search. Blind search. Heuristic search algorithms. Logical agents. First order predicate logic. Modal and temporal logic. Deductive and nondeductive reasoning methods. Designing contradictory and undefined systems. Possible worlds. Damster Shafer theory. Ad-hoc and heuristic learning methods. Structured knowledge. Knowledge presentation.

Student requirements

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Monitoring of students

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Obligatory literature

1. 1 Russel, S.; Norvig, P. Artificial Intelligence: A Modern Approach Prentice Hall, 2000.


Pretraži literaturu na:

Recommended additional literature

1. 1 Jović F. Expert Systems in Process Control Chapman and Hall, London, 1992.

2. 2 Patterson D.W. Introduction to Artificial Intelligence and Expert Systems Prentice Hall Int. 1990.

3. 3 Russel S. i Norvig P. Artificial Intelligence: A Modern Approach Prentice Hall 2000

Course assessment

Conducting university questionnaires on teachers (student-teacher relationship, transparency of assessment criteria, motivation for teaching, teaching clarity, etc.). Conducting Faculty surveys on courses (upon passing the exam, student self-assessment of the adopted learning outcomes and student workload in relation to the number of ECTS credits allocated to activities and courses as a whole).

Overview of course assesment

Learning outcomes
Upon successful completion of the course, students will be able to:

1. design, define and describe intelligent agent features for a specific problem solving task

2. develop a problem solving algorithm costumised for a specific agent

3. design, solve and evaluate the solution of a problem recorded in the first order logic

4. present information (knowledge) in a form suitable for processing by an agent

5. identify process uncertainties and develop process plan with known uncertainty

6. formulate a problem solving algorithm tailored for execution by an agent

7. create a space state diagram and an action plan for an agent



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