Module also offered within study programmes:
General information:
Name:
Autonomous Systems
Course of study:
2019/2020
Code:
ZSDA-3-0298-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, informatyka techniczna i telekomunikacja, inżynieria biomedyczna, inżynieria mechaniczna, matematyka, nauki o zarządzaniu i jakości, nauki socjologiczne
Module summary

Foundations of artificial autonomous decision systems (AADS): theory, design and implementation. Focus on autonomous decision making, autonomy indices, anticipatory networks and systems. The course will provide information on existing and planned applications of AADS in robotics, autonomous vehicles, webcrawlers, automatic trade systems. The seminars will involve students in elaborating topics of their particular interests, providing practical aspects and focusing on programming AADS algorithms

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)
Skills: he can
M_U001 Practical skilla of designing and implementing decision algorithms for autonomous vehicles SDA3A_U01, SDA3A_U07 Case study
M_U002 Knowledge of real-life implementations of autonomous systems based on case studies and applications in mobile robotics, web bot programming, automatic trade systems SDA3A_U01, SDA3A_U06 Case study
Knowledge: he knows and understands
M_W001 Students will gain knowledge on the autonomous systems to the extent sufficient to design decision algorithms for such systems. Specific problems learned include the theory of freewill in artificial autonomous decision systems (AADS), anticipatory systems and networks, autonomy indices. SDA3A_W02 Scientific paper,
Presentation
M_W002 Course graduates will be able to assess the autonomy level of a given system SDA3A_W02, SDA3A_W01 Scientific paper
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
30 30 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
Skills
M_U001 Practical skilla of designing and implementing decision algorithms for autonomous vehicles + - - - - - - - - - -
M_U002 Knowledge of real-life implementations of autonomous systems based on case studies and applications in mobile robotics, web bot programming, automatic trade systems + - - - - - - - - - -
Knowledge
M_W001 Students will gain knowledge on the autonomous systems to the extent sufficient to design decision algorithms for such systems. Specific problems learned include the theory of freewill in artificial autonomous decision systems (AADS), anticipatory systems and networks, autonomy indices. + - - - - - - - - - -
M_W002 Course graduates will be able to assess the autonomy level of a given system + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 112 h
Module ECTS credits 5 ECTS
Udział w zajęciach dydaktycznych/praktyka 30 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 40 h
Realization of independently performed tasks 40 h
Examination or Final test 2 h
Module content
Lectures (30h):
Foundations of autonomous systems

This course will provide an insight into the rapidly developing field of autonomous systems (AS) and their real-life implementation and applications. The theoretical background will include the notions of freewill, consciousness, and creativity in autonomous systems. Different AS will be compared with autonomy indices presented during the course. The course will give insight into the methods of autonomous decision making in financial, strategic, and disaster prevention decision systems as well as in robotics. Relations to multi-level and hierarchical decision making will be pointed out, including Stackelberg games. A particular attention will be paid to the coordination of autonomous robotic swarms, teams and formations.

A part of the course will be devoted to presenting students’ own research on AS in form of a moderated seminar.

Lecture topics
1. Introduction: basic principles and notions of autonomous systems, specifically the artificial autonomous decision systems (AADS). Basic classes and types of such systems.
2. The notion of freewill in AADS. Multicriteria theory of freewill and creativity in AADS. Four levels of autonomy. Other autonomy indices. Assessment of the autonomy level of a given system.
3. Construction of causal networks for autonomous systems, decision trees in AADS.
4. Coping with uncertainty in AADS. Different types of uncertainty: random, fuzzy, possibilistic, lack of knowledge (grey) etc. and their combinations.
5. Hierarchical autonomous systems: multi-level optimization and decision algorithms, the problem of enabling. Case studies: systems with different autonomy levels depending on their position in the hierarchy.
6. Autonomy models in controlled discrete event systems: supervisory control and degrees of supervision. Systems of coupled autonomous automata. Shortest paths in hipergraphs, applications to programming optimal behavior of systems of autonomous automata.
7. Robot vision, fusion of different sensor information, world model building for autonomous vehicles.
8. Anticipatory systems and networks. Case studies: design and solution of anticipatory networks (Python, Matlab). Dynamic programming and optimal control of anticipatory robotic systems.
9. Introduction to game theory for autonomous systems. Traffic equilibrium for networked autonomous systems. Stackelberg games.
10. Coordination and cooperation in autonomous systems: different types of cooperation, coordination algorithms.
11. Swarms and formations of autonomous vehicles. Swarm coordination in autonomous anticipatory robots. Case studies with UAVs and GUAVs. Anticipatory network-based coordination.
12. Application of anticipatory systems and networks to autonomous planning and backcasting.
13. Methods of simulation of autonomous vehicles.
14. Machine consciousness. Models and future impact.
15. Case studies: new generation autonomous planetary rover, design of an autonomous decision system with a given autonomy level.
A more detailed description of this course’s topics can be requested from the responsible teacher. See also the recommended literature

Additional information
Teaching methods and techniques:
  • Lectures: Treści prezentowane na wykładzie są przekazywane w formie prezentacji multimedialnej w połączeniu z klasycznym wykładem tablicowym wzbogaconymi o pokazy odnoszące się do prezentowanych zagadnień.
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 autonomous-system-related problem can be prepared as a homework. In case of the grade 2,0, students have the right to pass the final 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: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego.
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• (final examination or presentation grade) + 0,8• (report grade)
The presentation of own work may replace the final examonation, provided that the student participated iactively in at least 80% of lectures and/or seminars.

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

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:

Listeners should be familiar with basic optimization theory and operational research.
Practical knowledge of Matlab and Simulink is assumed. Other programming languages such as C++, C#, Python, or Java may be helpful but not as a requirement.
Some prior knowledge of multicriteria decision theory and of a robotic operational system will be additional assets.
Students who selected previously the course on “Advanced Multicriteria Optimization” or “Decision Support Systems” are particularly encouraged to enroll.

Recommended literature and teaching resources:

1. Andrzej M.J. SKULIMOWSKI (2019). Selected methods, applications, and challenges of multicriteria optimization. Seria: Monografie Komitetu Automatyki i Robotyki Polskiej Akademii Nauk [Scientific Monographs of the Automatics & Robotics Committee of the Polish Academy of Sciences], AGH Scientific Publishers, ISSN 1640-8969, ISBN 978-83-7464-628-4, p. 380.

2. 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, Crete, Greece. IEEE, Piscataway, s. 343–348. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6878810

3. Andrzej M.J. SKULIMOWSKI (2014b). Future prospects of human interaction with artificial autonomous systems. In: Abdelhamid Bouchachia (ed.): Adaptive and Intelligent Systems: third International Conference, ICAIS 2014, Bournemouth, UK, September 8–10, 2014. Proceedings. Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence 8779, Springer International Publishing, Berlin–Heidelberg, s. 131–141

4. Andrzej M.J. SKULIMOWSKI (2014c). 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, ed. (2013a). Looking into the future of creativity and decision support systems: Proceedings of the 8th International Conference on Knowledge, Information and Creativity Support Systems, Kraków, Poland, November 7–9, 2013, Progress & Business Publishers, Kraków, © 2013 (CD), 2014 (hardbound), s. 671, Advances in Decision Sciences and Future Studies, Vol. 2, ISBN: 978-83-912831-6-5, e-ISBN: 978-83-912831-8-9.

6. Andrzej M.J. Skulimowski (2013b). Universal Intelligence, Creativity, and Trust in Emerging Global Expert Systems. W: Rutkowski, L.; Korytkowski, M.; Scherer, R.; Tadeusiewicz, R.; Zadeh, L.A.; Zurada, J.M. (ed.). 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, p.582-592.

7. Andrzej M. J. SKULIMOWSKI (2012). Hybrid anticipatory networks. W: Artificial Intelligence and Soft Computing : 11th International Conference, ICAISC 2012: Zakopane, Poland, April 29–May 3, 2012. Proceedings, Part. 2, red. Leszek Rutkowski [et al.]. Berlin ; Heidelberg : Springer-Verlag. Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence 7268, s. 706–715.

8. Andrzej M. J. SKULIMOWSKI (2011). Freedom of choice and creativity in multicriteria decision making. W: Knowledge, Information, and Creativity Support Systems : 5th international conference, KICSS 2010 : Chiang Mai, Thailand, November 25–27, 2010 : revised selected papers, red. Thanaruk Theeramunkong [et al.]. Berlin ; Heidelberg : Springer-Verlag. Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, 6746, s. 190–203.

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

1. Andrzej M.J. SKULIMOWSKI (2016). The art of anticipatory decision making. in: S. Kunifuji, George A. Papadopoulos, A.M.J. Skulimowski, J. Kacprzyk (eds.). KICSS 2014: 9th International Conference on Knowledge, Information and Creativity Support Systems, Limassol, Cyprus, November 6–8, 2014, Proceedings, Advances in Intelligent Systems and Computing, Vol. 416, pp. 17-35, Springer-Verlag
2. 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, Crete, Greece. IEEE, Piscataway, pp. 343–348.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6878810
3. Andrzej M.J. SKULIMOWSKI (2014b). Future prospects of human interaction with artificial autonomous
systems. In: Abdelhamid Bouchachia (ed.): Adaptive and Intelligent Systems: third International
Conference, ICAIS 2014, Bournemouth, UK, September 8–10, 2014. Proceedings. Lecture Notes in
Computer Science, Lecture Notes in Artificial Intelligence 8779, Springer International Publishing,
Berlin–Heidelberg, pp. 131–141
4. Andrzej M.J. SKULIMOWSKI (2014c). 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 (2014d). Anticipatory networks and superanticipatory systems. CASYS:
International Journal of Computing Anticipatory Systems, Vol. 30, 117–130.
6.Andrzej M.J. SKULIMOWSKI, ed. (2013a). Looking into the future of creativity and decision support
systems: Proceedings of the 8th International Conference on Knowledge, Information and Creativity
Support Systems, Kraków, Poland, November 7–9, 2013, Progress & Business Publishers, Kraków,
2013 (CD), 2014 (hardbound), p. 671, Advances in Decision Sciences and 8Future Studies, Vol. 2, ISBN:
978-83-912831-6-5, e-ISBN: 978-83-912831-8-9.
7. Andrzej M.J. Skulimowski (2013b). 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. (ed.). 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.
8. Andrzej M.J. Skulimowski (2013c). Exploring the Future with Anticipatory Networks. In: Physics,
Computation, and the Mind – Advances and Challenges at Interfaces: Proc. of the 12th Granada Seminar
on Computational and Statistical Physics, 17–21.09.2012, La Herradura, Spain. Pedro L. Garrido, Joaquín
Marro, Joaquín J. Torres, J.M. Cortés (Eds.), American Institute of Physics, AIP Conf. Proc.1510, pp. 224-
233. http://scitation.aip.org/proceedings/volume.jsp
9.Andrzej M. J. SKULIMOWSKI (2012). Hybrid anticipatory networks. In: Artificial Intelligence and Soft
Computing: 11th International Conference, ICAISC 2012: Zakopane, Poland, April 29–May 3, 2012.
Proceedings, Part. 2, ed. Leszek Rutkowski [et al.]. Berlin ; Heidelberg : Springer-Verlag. Lecture Notes
in Computer Science. Lecture Notes in Artificial Intelligence 7268, pp. 706–715.
10. Andrzej M. J. SKULIMOWSKI (2011). Freedom of choice and creativity in multicriteria decision making.
In: Knowledge, Information, and Creativity Support Systems : 5th international conference, KICSS 2010 :
Chiang Mai, Thailand, November 25–27, 2010 : revised selected papers, ed. Thanaruk Theeramunkong
[et al.]. Berlin; Heidelberg : Springer-Verlag. Lecture Notes in Computer Science. Lecture Notes in
Artificial Intelligence, 6746, pp. 190–203.
11. Andrzej M. J. SKULIMOWSKI (2009). Formal models of freedom of choice and cognitive aspects of
multicriteria decision support. In: Człowiek i jego decyzje, 1, ed. Kazimierz Albin Kłosiński, Adam Biela.
Lublin, KUL, 2009. ISBN 978-83-7363-936-2, pp. 47–59.
12. Paweł Rotter, Andrzej M. J. SKULIMOWSKI (2009). Preference extraction in image retrieval. In:
Artificial intelligence for maximizing content based image retrieval, Zongmin Ma. Hershey; New York :
Information Science Reference, 2009. ISBN 978-1-60566-174-2, pp. 237–262.
13. Paweł Rotter, Andrzej M. J. SKULIMOWSKI (2008). A new approach to interactive visual search with
RBF networks based on preference modelling. In: Artificial Intelligence and Soft Computing – ICAISC
2008: 9th International Conference: Zakopane, Poland, June 22–26, 2008: proceedings, eds. Leszek
Rutkowski [et al.]. Berlin, Heidelberg: Springer-Verlag. Lecture Notes in Computer Science. Lecture
Notes in Artificial Intelligence, 5097, pp. 861–873.
14. Andrzej M. J. SKULIMOWSKI, Paweł Rotter (2006). Algorithms of the context contours approximation
in autonomous systems of images interpretation. W: Technology Transfer in Computer Science and
Automatic Control, ed. Andrzej M. J. Skulimowski. Kraków: Progress and Business Publishers, 2006
[reprinted 2008]. ISBN-10: 83-912831-3-5 ; ISBN-13: 978-83-912-831-3-4, pp. 119–167
15. Andrzej M. J. SKULIMOWSKI, Paweł Rotter (2006). Architecture of interactive image recognition
systems. In: Technology Transfer in Computer Science and Automatic Control, ed. Andrzej M. J.
Skulimowski. Kraków : Progress and Business Publishers, 2006 [ed. 2008]. ISBN-10: 83-912831-3-5 ;
ISBN-13: 978-83-912-831-3-4, s. 101–117.
16. Andrzej M. J. SKULIMOWSKI, Paweł ROTTER (2006). Application of the Hausdorff distance for the
recognition of two-dimensional objects with unique features. In: Technology Transfer in Computer
Science and Automatic Control, ed. Andrzej M. J. Skulimowski. Kraków : Progress and Business
Publishers, 2006 [ed. 2008]. ISBN-10: 83-912831-3-5 ; ISBN-13: 978-83-912-831-3-4, pp. 169–244.
17. Paweł ROTTER, Andrzej M. J. SKULIMOWSKI (2005). Information feedback and preference
approximation in image retrieval systems. In: KKA 2005 : XV Krajowa Konferencja Automatyki, Warsaw,
27–30 June 2005, Vol. 3, ed. Zdzisław Bubnicki, Roman Kulikowski, Janusz Kacprzyk. Warszawa: Instytut
Badań Systemowych Polskiej Akademii Nauk, pp. 63–68.
18. Andrzej M.J. SKULIMOWSKI (1994). Optimal strategies for quantitative data retrieval in distributed
database systems. Proceedings of the Second International Conference on Intelligent Systems
Engineering, Hamburg, 5-9 September 1994; IEE Conference Publication No. 395, IEE, London; ISBN 0-
85296-621-0, pp. 389–394 (IEEE Xplore:
ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=332005).
19. Andrzej M.J. SKULIMOWSKI (1994). Optimizing the structure of a partitioned population. In: System
modelling and optimization: proceedings of the 16th IFIP-TC7 conference : Compiegne, France, July 5–9,
1993, ed. J. Henry, J.-P. Yvon. London: Springer-Verlag, 1994. Lecture Notes in Control and
Information Sciences, LNCIS 197. Springer-Verlag Berlin Heidelberg New York, pp. 771–782.
20. Andrzej M.J. SKULIMOWSKI, B.F. Schmid (1992). Redundancy-free description of partitioned complex
systems. Mathematical and Computer Modelling, 16(10), 71-92 [www.sciencedirect.com – open access]
21. Andrzej M.J. SKULIMOWSKI (1991). Optimal Control of a Class of Asynchronous Discrete-Event
Systems. In: Automatic Control in the Service of Mankind. Proceedings of the 11th IFAC World
Congress, Tallinn (Estonia), August 1990, Vol.3, pp. 489-495; Pergamon Press, London.
22. A.M.J. SKULIMOWSKI (1987). An Interactive Modification of the Decision Set to Attain a Target Point in
Vector Optimization Problems. VII-th International Conference on Multicriteria Decision Making, Kyoto
(Japan), 18-22.08.1986. In: Y. Sawaragi, K. Inoue, H. Nakayama (eds.), Toward Interactive and Intelligent
Decision Support Systems, Vol. 1, Proceedings, Lecture Notes in Economics and Mathematical Systems,
285, Springer-Verlag, Berlin-Heidelberg-New York-London-Paris-Tokyo, pp.142-153.
23. Andrzej M. J. SKULIMOWSKI (1985). Mathematical Bases for the Numerical Evaluation of the
Hausdorff Distance. Proceedings of the 11th IMACS World Congress, Oslo (Norway), August 5-9, 1985;
Vol.5, pp. 343-346.
24. Andrzej M. J. SKULIMOWSKI (1985). Solving Vector Optimization Problems via Multilevel Analysis of Foreseen Consequences. Found. Control Engrg.,10, No.1, 25-38 (www.Researchgate.net)

Additional information:

Students are invited to participate in autonomous-system-related research carried out at the Decision Science Lab of the AGH UST