Module also offered within study programmes:
General information:
Name:
Expert systems
Course of study:
2019/2020
Code:
ZSDA-3-0288-s
Faculty of:
Szkoła Doktorska AGH
Study level:
Third-cycle studies
Specialty:
-
Field of study:
Szkoła Doktorska AGH
Semester:
0
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Responsible teacher:
prof. dr hab. inż. Skulimowski Andrzej M. (ams@agh.edu.pl)
Dyscypliny:
automatyka, elektronika i elektrotechnika, informatyka techniczna i telekomunikacja, inżynieria biomedyczna
Module summary

This course will familiarize PhD.students with the principles and methods of expert systems, in particular with the expert systems architecture, classification and most relevant applications. The students will acquire knowledge on inference engines, knowledge bases, uncertainty management, intelligent user interfaces, expert information fusion and other topics that will allow them to become effective expert systems users.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 Students are capable of participating in expert knowledge elicitation from a group of experts, in expert panels; they can use information fusion tools and understand the relevance of consensus building processes among experts and expert systems SDA3A_K02 Activity during classes
Skills: he can
M_U001 Students can select an appropriate expert system's to solve a given problem in the area of their research or professional interests SDA3A_U01
Knowledge: he knows and understands
M_W001 Students can design an expert system's composed of a knowledge base, inference engine, recommendation procedure and GUI SDA3A_W02 Scientific paper
M_W002 Students know relevant reeasoning methods used in rule-based expert systems and how to verify the consistency of the set of rules SDA3A_W01 Presentation
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
28 28 0 0 0 0 0 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 Students are capable of participating in expert knowledge elicitation from a group of experts, in expert panels; they can use information fusion tools and understand the relevance of consensus building processes among experts and expert systems - - - - - - - - - - -
Skills
M_U001 Students can select an appropriate expert system's to solve a given problem in the area of their research or professional interests - - - - - - - - - - -
Knowledge
M_W001 Students can design an expert system's composed of a knowledge base, inference engine, recommendation procedure and GUI + - - - - - - - - - -
M_W002 Students know relevant reeasoning methods used in rule-based expert systems and how to verify the consistency of the set of rules - - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 87 h
Module ECTS credits 3 ECTS
Udział w zajęciach dydaktycznych/praktyka 28 h
Preparation for classes 12 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 30 h
Realization of independently performed tasks 15 h
Examination or Final test 2 h
Module content
Lectures (28h):
An application-oriented introduction to modern expert systems and related AI methods

1. Introduction to expert systems (ES) as one of the AI cornerstones: basic notions and trends, natural vs. artificial expertise
2. Principles of expert systems architecture, design, and classification
3. Knowledge bases as a major component of ES, knowledge representation, description logics and ontologies,
4. Foundations of ES logics, production rules, inference models and causation, rule- based ES
5. ES inference engines: forward and backward rule processing in diagnostic systems –
practical issues, introduction to Prolog programming
6. Case-based reasoning principles and their integration with diagnostic ES
7. Uncertainty modeling in expert systems: probabilistic inference rules, Bayesian networks, fuzzy numbers, sets and rules, rough sets
8. Uncertain knowledge representation: Hartley information measures, Möbius transform, belief functions and Dempster-Shafer possibility theory
9. Methods of reasoning about the future: probabilistic and fuzzy anticipatory networks
10. Trust and credibility management in ES. Principles of trust- and credibility based information fusion in ES: linking human and artificial ES expertise in a single information processing models
11. Neural computing in ES. Feedforward networks and backpropagation, self- organizing maps (SOM)
12. Intelligent decision analysis in ES. The bridge to IDSS: recommendations and their further use in IDSS
13. Salient applications of ES in medicine, business rule processing, technical diagnostics, and others
14. Global expert systems (GES) and global brain: the perspectives of ES and their fusion with human brains.
Future prospects of ES methods and applications (moderated seminars)

Additional information
Teaching methods and techniques:
  • Lectures: Lectures with active participation of students, slide and software presentations as well as different audio-visual tools willl be used
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Students receive grades for the presentation and the final semester report. The report presenting a solution to an expert-system-related problem can be prepared as a homework. In case of the grade 2,0, students have the right to pass the examination for the second time on a day specified at least 7 days beforehand.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Students are encouragesd to initiate discussions and propose problems to be solved with expert systems methods and tools
Method of calculating the final grade:

The grade for the final report will be granted based on the number of points received, according to the rule that 50% of the maximum no. of points is necessary to pass (grade 3,0). All other grades are assigned according to the linear scale.
The final grade is the weighted average of these grades calculated according to the formula: Final grade= 0,2• (presentation or examination grade) + 0,8• (report grade)

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Absence at up to three lectures or seminars are possible without a specific explanation. In case of absence from reasons independent from the student, such as illness or injury, the students may benefit from additional consulting hours with the lecturer

Prerequisites and additional requirements:

The knowledge of logics, algebra, probability, and higher-level computer programming languages are necessary to fully benefit from this course

Recommended literature and teaching resources:

1. S. Russel, P. Norvig (2009). Artificial Intelligence: A Modern Approach (3rd Edition), Prentice Hall, URL: aima.cs.berkeley.edu
2. Andrzej M.J. Skulimowski (2019). Selected methods and applications of multicriteria optimization. Komitet Automatyki i Robotyki PAN, Nr 19, Wyd. AGH, Kraków, s.380
3. Andrzej M.J. Skulimowski (2014). Anticipatory network models of multicriteria decision-making processes. International Journal of Systems Science 45(1), 39-59, http://www.tandfonline.com/doi/full/10.1080/00207721.2012.670308
4. Andrzej M.J. Skulimowski (2013). Universal Intelligence, Creativity, and Trust in Emerging Global Expert Systems. In: Rutkowski, L.; Korytkowski, M.; Scherer, R.; Tadeusiewicz, R.; Zadeh, L.A.; Zurada, J.M. (Eds.). Artificial Intelligence and Soft Computing. 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part II. Lecture Notes in Computer Science. Lecture
Notes in Artificial Intelligence 7895, Springer-Verlag, pp.582-592, https://doi.org/10.1007/978-3-642-38610-7_53
5. Andrzej M.J. Skulimowski (2017a). Cognitive content recommendation in digital knowledge repositories – a survey of recent trends. W: Artificial Intelligence and Soft Computing: 16th International Conference (ICAISC 2017), Zakopane, Poland, June 11–15, 2017, proceedings, Part 2, eds. Leszek Rutkowski et al., —: Springer International Publishing, Switzerland. Lecture Notes in Computer Science, LNAI 10246, s. 574–588, https://doi.org/10.1007/978-3-319-59060-8_52
6. Andrzej M.J. Skulimowski (2017b). Expert Delphi Survey as a Cloud-Based Decision Support Service, IEEE 10th International conference on Service-Oriented Computing and Applications SOCA 2017, 22–25 Nov. 2017, Kanazawa, Japan. IEEE, Piscataway, s. 190–197, http://ieeexplore.ieee.org/document/8241542.

Scientific publications of module course instructors related to the topic of the module:

1. Andrzej M. J. SKULIMOWSKI (2012). Discovering complex system dynamics with intelligent data retrieval tools. In: Intelligent Science and Intelligent Data Engineering: second Sino-foreign-interchange workshop, IScIDE 2011: Xi’an, China, October 23–25, 2011: revised selected papers, eds. Yanning Zhang [et al.]. Berlin, Heidelberg: Springer-Verlag, Lecture Notes in Computer Science 7202, pp. 614–626. http://link.springer.com/chapter/10.1007/978-3-642-31919-8_78
2.Andrzej M.J. Skulimowski (2013). Universal Intelligence, Creativity, and Trust in Emerging Global Expert Systems. In: Rutkowski, L.; Korytkowski, M.; Scherer, R.; Tadeusiewicz, R.; Zadeh, L.A.; Zurada, J.M. (Eds.). Artificial Intelligence and Soft Computing. 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part II. Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence 7895, Springer-Verlag, pp. 582-592, https://doi.org/10.1007/978-3-642-38610-7_53
3. Andrzej M.J. SKULIMOWSKI (2014a). An insight into the evolution of intelligent information processing technologies until 2025. In: IISA 2014: 5th International Conference on Information, Intelligence, Systems and Applications: 7–9 July, 2014, Chania, Greece. IEEE, pp. 343–348. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6878810
4. Andrzej M.J. SKULIMOWSKI (2014b). Anticipatory network models of multicriteria decision-making processes, International Journal of Systems Science, Vol. 45 (1), 39-59, DOI:10.1080/00207721.2012.670308, [http://www.tandfonline.com/doi/full/10.1080/00207721.2012.670308]
5.Andrzej M.J. SKULIMOWSKI (2016a). Impact of future intelligent information technologies on the methodology of scientific research. In: Proceedings 16th IEEE International Conference on Computer and Information Technology, Nadi, Fiji, IEEE CPS, pp. 238–247, 7–10 December 2016. doi: 10.1109/CIT.2016.118
6. Andrzej M.J. SKULIMOWSKI (2016b). The role of creativity in the development of future intelligent decision technologies. In: Knowledge, information and creativity support systems: recent trends, advances and solutions: selected papers from KICSS’2013 – 8th international conference on Knowledge, Information, and Creativity Support Systems, November 7-9, 2013, Kraków, Poland, eds. Andrzej M. J. Skulimowski, Janusz Kacprzyk. Advances in Intelligent Systems and Computing, vol. 364, Springer International Publishing, Switzerland, ISBN: 978-3-319-19089-1, pp. 279–297, https://doi.org/10.1007/978-3-319-19090-7_22.
7. Andrzej M.J. Skulimowski (2017a). Cognitive content recommendation in digital knowledge repositories – a survey of recent trends. W: Artificial Intelligence and Soft Computing: 16th International Conference (ICAISC 2017), Zakopane, Poland, June 11–15, 2017, proceedings, Part 2, eds. Leszek Rutkowski et al., —: Springer International Publishing, Switzerland. Lecture Notes in Computer Science, LNAI 10246, s. 574–588, https://doi.org/10.1007/978-3-319-59060-8_52
8. A.M.J. Skulimowski (2017b). Expert Delphi Survey as a Cloud-Based Decision Support Service, IEEE 10th International conference on Service-Oriented Computing and Applications SOCA 2017, 22–25 Nov. 2017, Kanazawa, Japan. IEEE, Piscataway, s. 190–197, http://ieeexplore.ieee.org/document/8241542.
9. Andrzej M.J. SKULIMOWSKI (2019). Anticipatory networks. In: Poli R. (ed.), Handbook of anticipation: theoretical and applied aspects of the use of future in decision making. Springer International Publishing AG, Cham, pp. 995–1030. https://link.springer.com/referenceworkentry/10.1007/978-3-319-31737-3_22-1
10. Andrzej M.J. SKULIMOWSKI (2018). Strategy building for a knowledge repository with a novel expert information fusion tool. In: 6th International Conference on Future-Oriented Technology Analysis (FTA): Future in the making : 4-5 June 2018, Brussels. pp.1–15, https://ec.europa.eu/jrc/sites/jrcsh/files/fta2018-paper-c2-skulimowski.pdf

Additional information:

Students are invited to participate in expert-system-related research and expert systems applications in finance carried out by the Student Scientific Society on Financial Modelling (www.knwmf.agh.edu.pl)