|
Lecturer(s)
|
|
|
|
Course content
|
Course contents and syllabus: 1. Introduction - agent approach to autonomous systems, UI areas with impact and usability in autonomous systems. 2. Environment and its representation - predicate representation, object approach, languages for autonomous systems. 3. Agent interaction with the environment - active sensors, active effectors, localization. 4. Formulation of goals and planning with UI - types of goals and evaluation of progress, STRIPS, navigation and movement, optimization and genetic algorithms. 5. Machine learning - types of learning, case based reasoning, decision trees, artificial neural networks in robotics. 6. Human - robot interaction - use of UI for HRI. 7. Distributed systems - multiagent systems and agent communication, intelligent systems and their management and coordination. The practical part of the course (tutorials) copies the content of lectures. During the tutorials, students will apply and practice theoretical knowledge from lectures. The use of teamwork and project teaching is also expected.
|
|
Learning activities and teaching methods
|
Monologic (reading, lecture, briefing), Demonstration, Laboratory
- Preparation for exam
- 20 hours per semester
- Semestral paper
- 20 hours per semester
- Preparation for classes
- 34 hours per semester
|
|
Learning outcomes
|
The course is focused on topics usually included in the field of artificial intelligence, and their use in robotics and autonomous and embedded systems. The aim of the lectures is to introduce students to selected methods from these areas. Emphasis will be placed on the classical agent concept of autonomous systems. Topics related to human-robot interaction and the issue of coordination of autonomous systems will also be included. As part of the exercise, students will learn to implement selected procedures, both in the environment of simulation models and in real robotic systems.
Upon completion of the course the student will have knowledge of interrelated areas of autonomous systems and artificial intelligence and advanced data processing of various data types. Student will be able to to practically use this knowledge and implement it both in the simulation environment and in real robot systems and robots.
|
|
Prerequisites
|
A prerequisite for enrollment in this course is knowledge of programming, at least at the level of completing the course OOP I and II.
|
|
Assessment methods and criteria
|
Written examination, Analysis of student's work activities (technical works), Test, Seminar work
To complete the course, it is necessary to solve continuous tasks in tutorials, pass a theoretical test, develop and implement a semester project and pass an oral exam.
|
|
Recommended literature
|
-
BHAUMIK, Arkapravo. From AI to robotics: mobile, social, and sentient robots. CRC Press, 2018. ISBN 9781482251487.
-
GOVERS, Francis X. Artificial intelligence for robotics: Build intelligent robots that perform human tasks using AI techniques. Packt Publishing Ltd, 2018. ISBN 9781788835701.
-
MURPHY, Robin R. Introduction to AI robotics. MIT press, 2019. ISBN 9780262038485.
-
RUSSELL, Stuart J.; NORVIG, Peter. Artificial intelligence: a modern approach. 4th ed. Pearson, 2020. ISBN 9781292401133.
|