Artificial Intelligence and Expert Systems
- Credits: 4
- Ending: Examination
- Range: 0P + 2C
- Semester: winter
- Year: 3
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
After studying this course, students gain the knowledge and should be able to:
A. have an orientation in the field of Artificial Intelligence and its use in business practice
B. understand the principles of declarative programming and the differences in the creation of such a program compared to procedural programming;
C. understand the needs of different representations of knowledge in AI so that they can be easily processed through IT for non-deterministic decision-making tasks.
D. distinguish knowledge tasks in practice, analyze them and suggest a type of system that could solve them;
E. have an orientation in the use of knowledge and expert systems in practice with regard to their basic functions;
F. understand the basic techniques of acquiring knowledge from a domain expert and the life cycle of creating an expert system in business practice;
G. answer the basic questions of knowledge engineering in the deployment of systems in practice;
H. create simple programs in a language that supports the rule paradigm and understand the connection with interfaces to applications created in other programming languages;
Indicative content
1. Introduction to artificial intelligence, history, new technologies, the importance of artificial intelligence in practice.
2. Definition of the terms data, information, knowledge, competencies in the company and their connection with the structuring in informatics. Programming introduction in CLPS
3. The concept of knowledge agent, the principles of its operation and the basic architecture of the agent, declarative programming. Differences between declarative and structured programming.
4. State space search, basic search algorithms and their connection with the operation of the knowledge agent. Relationship between state space search and the CLIPS environment.
5. Knowledge base and knowledge representation, types of knowledge representations from logic to rule-based systems. Working with rule-based systems and lists (multifield values) in CLIPS. Comparing patterns for multifield values, examining the conditions in the rule.
6. From semantic networks and frame-based knowledge representation to object system modeling. Use of knowledge representations in informatics. Creating frames and classes in CLIPS and the basic multifield functions.
7. Knowledge tasks classification and their characteristics. Knowledge and expert systems, differences in architecture. Explanatory module and its meaning in ES. User functions in CLIPS, calling them and using them in rules.
8. Expert systems and introduction to knowledge engineering. Importance of Expert Systems in practice (e.g. Business rule Engine). Explanatory artificial intelligence and its importance in machine learning.
9. Creating examples in the COOL environment, classes, instances and sending messages between classes, the basic OO philosophy of modeling in artificial intelligence and its meaning, pattern-matching with objects.
10. Non-standard functions in CLIPS, their use in specific examples (forall, exists, foreach, do-for-all-facts, etc.) Comparison of different solutions in examples.
11. Programming trees using rules, the influence of rule conditions on program operation, preparation of tasks for projects. Management tasks such as planning, scheduling, diagnostics, prediction, assignment, evaluation and their modeling.
12. Working in groups on final projects, working with modeling of knowledge engineering.
13. Presentation of group final projects. Examples of professional tools for developing expert systems.
Support literature
1. Návrat, P. a kol. Umelá inteligencia, STU, Edícia učebných textov informatiky a informačných technológií, 2011
2. Kelemen, J. Pozvanie do znalostnej spoločnosti, IURA Edition, 2007
3. Dvořák, J., Expertní systémy, 2004. Dostupné na: http://www.uai.fme.vutbr.cz/~jdvorak/Opory/ExpertniSystemy.pdf (dostupné 21.10.2021)
4. Svátek, V. Ontologie a www. Dostupné na: http://nb.vse.cz/~svatek/onto-www.pdf (dostupné 20.10.2021)
5. Riley, G. CLIPS- A Tool for Building Expert Systems, 2013, Dostupné na: http://clipsrules.sourceforge.net/ (dostupné 20.10.2021)
6. Negnevitsky, M., Artificial Intelligence A Guide to Intelligent Systems, Pearson, 2011
7. Benson, M., Handbook of Expert Systems, Clanrye Intl, 2015
8. Norvig, P., Russell, S., Artificial Intelligence: A Modern Approach, Global Edition, 2021
9. Elektronický kurz Umelá inteligencia a expertné systémy, LMS Moodle Ekonomickej Univerzity, dsotupné na: https://moodle.euba.sk/course/view.php?id=2
Requirements to complete the course
Requirements to complete the course:
- final exam - written form, 60% (passing the exam means obtaining 51% from the evaluation of exam) The exam consists of two parts: verification of theoretical knowledge (test with different types of questions). The theoretical part verifies the achieved level of educational results A., D, E, F, G. Verification of practical skills (program creation in CLIPS), where the level of educational results B, C, H is verified.
Seminars
- individual work and continous tests 15%,
- working in small teams: elaboration and seminar topic presentation 10%, work over the final project 15%
Together: 40%
By evaluating individual work and evaluating work in teams, the following educational results are developed and evaluated: B., C., D., G., H.
Student workload
Total study load (in hours):
3 credits x 26 hours= 78 hours
Study load distribution:
Seminar participation: 26 hours
Preparation for seminars: 8 hours
Project preparation: 10 hours
Preparation for the final exam: 34 hours
Language whose command is required to complete the course
slovak
Date of approval: 11.03.2024
Date of the latest change: 12.12.2022