Course detail

Expert Systems and Languages for Artificial Intelligence

FSI-VES-KAcad. year: 2019/2020

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.

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.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Mařík, V. a kol. Umělá inteligence. Praha, Academia.
Giarratano, J., Riley, G. Expert Systems. Principles and Programming. Boston, PWS Publishing Company 1998.
Jackson, P. Introduction to Expert Systems. Harlow, Addison-Wesley 1999.
Seibel, P. Practical Common Lisp. Apress, 2005. http://www.gigamonkeys.com/book/
Kelemen J. a kol. Tvorba expertních systémů v prostředí CLIPS. Praha, Grada 1999.
Luger, G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Harlow, Addison-Wesley 2008.
Merrit, D. Building Expert Systems in Prolog. Berlin, Springer-Verlag 1989. http://www.amzi.com/ExpertSystemsInProlog/index.htm
Bratko, I. Prolog Programming for Artificial Intelligence. Pearson Education 2011.
Luger, G.F.; Stubblefield, W.A. AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java. Addison-Wesley 2008.

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.

Language of instruction

Czech

Work placements

Not applicable.

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.

Classification of course in study plans

  • Programme M2I-K Master's

    branch M-AIŘ , 1. year of study, summer semester, 3 credits, compulsory-optional
    branch M-AIŘ , 1. year of study, summer semester, 3 credits, 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.

Controlled Self-study

17 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.

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