Rule-based and Expert Systems 510-IS2-1SE-23
Profile of studies: general academic
Form of studies: full-time
Course type: obligatory
Field and discipline of science: exact and natural sciences, computer science
Year of studies/semester: 1 / 2
Preliminary requirements (so-called sequential system of classes and examinations): ---
Number of hours of classes:
- lecture: 15 h
- laboratory classes: 15 h
Didactic methods: lecture, presentation, discussion, project, office hours.
ECTS credits: 3
Details of student's workload (activity and number of hours):
- Attendance at classes:
- - lecture : 15 h
- - laboratory: 15 h
- Preparation for classes:
- - lecture: 2 h
- - laboratory: 6 h
- Literature studying: 8 h
- Student's own works on computer programs, projects, reports, presentations, homeworks: 10 h
- Preparation for exam: 7 h
- Attendance at exam: 2 h
- Attendance at office hours: 10 h
Quantitative indicators:
- Student's workload related to activities that require direct participation of a teacher: 42 h / 1,7 ECTS
- Student's workload related to activities that do not require direct participation of a teacher: 33 h / 1,3 ECTS
Type of course
Mode
(in Polish) zdalnie
Course coordinators
Learning outcomes
- Knows and can explain architectures and applications of expert (knowledge-based) /rule-based information systems. KP7_WG6, KP7_WK1
- Knows and can characterize essential methods for representing knowledge and reasoning, and for defining and processing business rules. KP7_WG6
- Is able to identify and analyze a problem that can be solved by using a knowledge/rule-based system, and to design essential parts of a system: a knowledge/rule base and inference/processing methods. KP7_UW15
- Is able to develop a complete solution that is based on a knowledge/rule-based system, and to present and document this solution. KP7_UW6
- Creatively solves problems related to system design and development. KP7_UO4
Assessment criteria
Credit type: graded
Students cannot be examined before passing the laboratory/exercises.
Rules of AI usage v.25.09.29
Students can use AI systems only to the extent permitted by law acts adopted by the University and Faculty, to automatize mechanic actions that require nether creative nor critical thinking, and that do not require understanding processes and technologies.
Students are prohibited from using AI to do tasks that aim to develop students' creativity, skills and knowledge, that should be solved without automatization in order to ensure the learning outcomes.
Questions and doubts about AI usage should be communicated to the teacher.
Bibliography
Essential bibliography:
- Niederliński A. , Systemy ekspertowe dla automatyzacji zarządzania, wyd. Skalmierski, 2017 [https://www.ue.katowice.pl/fileadmin/user_upload/wydawnictwo/Darmowe_E-Booki/Niederlinski_Systemy_ekspertowe_wyd_2.pdf]
- Jagielski J., Inżynieria wiedzy, Uniwersytet Zielonogórski, Zielona Góra, 2005
- Bownik L., Sieć Semantyczna, Reprezentacja i Logika, EMAG, 2009
- Salatino M., De Maio M., Aliverti E., Mastering JBoss Drools 6 for Developers, Packt Publishing, 2016
- Giarratano J.C., Riley G.D., Expert Systems: Principles and Programming, Course Technology, 2004
- documention, internet homepages of technologies like CLIPS, DROOLS, JESS
Supplementary readings:
- Mukherjee, C., Build Android-Based Smart Applications Using Rules Engines, NLP and Automation Frameworks, Apress, 2018
- Mulawka J. , Systemy ekspertowe, WNT, Warszawa, 1996
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: