Artificial Intelligent Systems

Within this course, students are introduced to the fundamental concepts and components of artificial intelligence, focusing on artificial perception and pattern recognition, soft computing, machine learning, autonomous agents, and ambient intelligence. The course covers intelligent problem-solving using graph representations and algorithms, with special emphasis on exhaustive and heuristic exploration of problem spaces. In the discussion of expert systems, attention is paid to the reasoning process, highlighting rule-chaining methods, propositional and predicate logic, as well as fuzzy and probabilistic reasoning. In learning from data using machine learning methods, multi-variable regression with artificial neural networks is primarily addressed.

The final part of the course is dedicated to multi-agent systems, introducing platforms and inter-agent communication languages that facilitate the development of such systems. 

The objective of the course is to provide the student with the knowledge of basic mathematical and computational approaches in the development of artificial intelligence. The student is acquainted with the basic concepts of artificial intelligent systems and with the examples of the implementations of such systems. The acquired knowledge of soft computing, machine learning, and knowledge-representation forms enables the basic design and development of expert systems, autonomous intelligent agents, as well as multi-agent applications.

The lectures provide a theoretical background of all the considered models and methods together with simple computational examples that illustrate the key characteristics of all the presented methods. Lectures notes with the slides from the lectures are available to the students as the basic study material. As part of the lectures, the students receive optional homework assignments including theoretical questions as well as computational exercises that enable the students to promptly verify the acquired knowledge. Practical work is carried out as part of the laboratory exercises, where the students solve the given programming tasks. As part of the laboratory exercises, students also carry out additional elective projects within which they implement selected artificial intelligent systems or one of their key components for the selected fields of application, or alternatively, they carry out in-depth studies of existing artificial intelligent systems. The results of the elective projects are reported in written reports.