Course detail
Expert Systems and Languages for Artificial Intelligence
FSI-VES-K Acad. year: 2019/2020 Summer semester
The course begins with a description of the principles of expert systems. It continues with the introduction to selected languages for artificial intelligence (Lisp, Clips and Prolog) and their use for solving problems of artificial intelligence including the creation of expert systems. In the final part of the course, selected expert systems are presented and methods of uncertainty processing are described.
Language of instruction
Czech
Number of ECTS credits
3
Supervisor
Department
Learning outcomes of the course unit
Knowledge of basic principles of working and building expert systems. Knowledge of functional, logic and rule-based programming. Ability to select and apply a suitable tool for expert system creation.
Prerequisites
Mathematical principles of computer science, mathematical logic and probability theory.
Planned learning activities and teaching methods
The course is taught through exercises focused to practical using the artificial intelligence languages (Lisp, Clips and Prolog) and to building expert systems.
Assesment methods and criteria linked to learning outcomes
Requirements for graded course-unit credit: active participation in seminars, completion of final test and elaboration of semester project.
Aims
The goal is for students to understand the principles of working expert systems, be familiar with the languages of artificial intelligence and be able using them to create expert systems.
Specification of controlled education, way of implementation and compensation for absences
Attendance at the seminars is required. An absence can be compensated for via solving given problems.
The study programmes with the given course
Programme M2I-K: Mechanical Engineering, Master's
branch M-AIŘ: Applied Computer Science and Control, compulsory-optional
Type of course unit
Guided consultation in combined form of studies
9 hours, compulsory
Teacher / Lecturer
Syllabus
1. Introduction to expert systems.
2. Introduction to Lisp language.
3. Solving problems in Lisp, application examples.
4. Building expert systems in Lisp.
5. Introduction to Clips language.
6. Solving problems in Clips, application examples.
7. Building expert systems in Clips.
8. Introduction to Prolog language.
9. Solving problems in Prolog, application examples.
10. Building expert systems in Prolog.
11. Examples of commercial and non-commercial expert systems.
12. Handling uncertainty in expert systems.
13. Evaluating of semester projects.