Učni načrt predmeta

Predmet:

Inteligentni sistemi in agenti

Course:

Intelligent Systems and Agents

Študijski program in stopnja / Study programme and level	Študijska smer / Study field	Letnik / Academic year	Semester / Semester
Informacijske in komunikacijske tehnologije, 3. stopnja	Inteligentni sistemi in robotika	1	1
Information and Communication Technologies, 3rd cycle	Intelligent Systems and Robotics	1	1

Vrsta predmeta / Course type

Izbirni / Elective

Univerzitetna koda predmeta / University course code:

IKT3-631

Predavanja Lectures	Seminar Seminar	Vaje Tutorial	Klinične vaje work	Druge oblike študija	Samost. delo Individ. work	ECTS
30	30			30	210	10

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:

prof. dr. Matjaž Gams

Sodelavci / Lecturers:

prof. dr. Mitja Luštrek

Jeziki / Languages:

Predavanja / Lectures:

Slovenščina, angleščina / Slovenian, English

Vaje / Tutorial:

Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:

Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second cycle studies in information or communication technologies or completed second cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:

Content (Syllabus outline):

Znanstvena metoda:
Strukture znanstvenega védenja, znanstvene aktivnosti in procesi

Uvod:
Motivacija, cilji, koristnost inteligence. Definicije naravne, inženirske, umetne inteligence. Študije inteligence. Višje stopnje razumevanja.

Informacijska družba:
Definicija informacijske družbe. Trendi informacijske družbe, lastnosti, primeri uporabe. Dileme v sedanji in prihodnji družbi. Inteligentne storitve in sistemi v informacijski družbi.

Umetna inteligenca:
Pregled umetne inteligence (po knjigi avtorjev Russel in Norvig).
Generativna umetna inteligenca (GPT).

Inteligentni sistemi:
Osnove inteligentnih sistemov. Inteligentni sistemi v poslovanju, tehniki, znanosti. Metode in tehnike inteligentnih sistemov. Primeri odmevnih aplikacij. Pregled sorodnih predmetov po svetu.

Inteligentni agenti:
Agenti kot osnovni gradniki umetne inteligence. Tipi agentov. Hierarhija agentov. Platforme injeziki. Repozitoriji agentov. Agenti e-poslovanja. Pomembne aplikacije agentov. Pregled sorodnih predavanj.

Ambientalna inteligenca:
Definicija ambientalne inteligence. Uporaba inteligentnih sistemov in agentov. Metode za reševanje pomembnejših nalog ambientalne inteligence. Primeri aplikacij in večjih sistemov.

Superinteligenca in princip mnogoterega znanja:
Definicije. Indikatorji. Posledice. Primeri snovanja sistemov.

Izzivi pri uvajanju inteligentnih sistemov in agentov:
Specifike pri uvajanju inteligentnih sistemovin agentov. Prednosti in slabosti v primerjavi s klasičnimi pristopi.

Scientific Method:
Scientific knowledge structures, scientific activities/processes

Introduction:
Motivation, goals, usefulness of intelligence. Definitions of natural, engineering, artificial intelligence. Studies of intelligence. Higher levels of understanding.

Information society:
Definition of information society. Trends of information society, properties, practical examples of use. Dilemmas in the current and future society. Intelligent services and systems in information society.

Artificial intelligence:
Short overview of AI (based on the book by Russel and Norvig).
Generative AI (GPT).

Intelligent systems:
Principles and motivations of intelligent systems. Intelligent systems in business, engineering, science. Methods and techniques of intelligent systems. Examples of killer applications. Overview or relevant courses at best institutions

Intelligent agents:
Agents as basic ingredients of artificial intelligence. Types of agents. Hierarchy of agents. Platforms and languages. Agent repositories. E-business agents. Overview of world-wide lectures.

Ambient intelligence:
Definition of ambient intelligence. Use of intelligent systems and agents. Approaches to major tasks in ambient intelligence. Examples of applications and systems.

Superintelligence and the principle of multiple knowledge:
Definitions. Indicators. Consequences. Examples of applications.

Challenges when introducing intelligent systems and agents:
Specifics of intelligent systems and agents. Advantages and weaknesses compared to classical approaches.

Temeljna literatura in viri / Readings:

Knjige / books:
- S. Russel, and P. Norvig. Artificial Intelligence: A Modern Approach, 4th Edition. Pearson Education Limited, 2021. ISBN-13 978-0-13-461099-3
- A.A. Hopgood. Intelligent Systems for Engineers and Scientists, 4th Edition. CRC Press, 2022. ISBN 978-1032126760
- T. Zwingmann. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Lear. 1st Ed., O'Reilly Media, ISBN 2022, 978-1098111472
- G. Weiss. Multiagent Systems (Intelligent Robotics and Autonomous Agents series). MIT, 2013. ISBN 978-0262018890
- D.L. Poole, and A.K. Mackworth. Artificial Intelligence: Foundations of Computational Agents. 3rd edition, Cambridge University Press, 2023. online.
- I. Goodfellow, and Y. Bengio. Deep Learning, MIT, 2016. SBN-13: 978-0262035613.
- N. Bostrom. Superintelligence: Paths, Dangers, Strategies, Oxford University Press, 2016. ISBN 978-0198739838
- P. Lee, C. Goldberg, I. Kohane. The AI Revolution in Medicine: GPT-4 and Beyond, 1st Edition, 2023, Pearson, ISBN 978-0138200138

Cilji in kompetence:

Objectives and competences:

Cilj predmeta je podati splošno in napredno znanje o inteligentnih sistemih in inteligentnih agentih v povezavi z umetno inteligenco, ambientalno inteligenco in informacijsko družbo. Uvodoma so predstavljeni osnovni koncepti omenjenih področij, cilji, motivacija, smisel, nameni in problemi pri uveljavljanju omenjenih metod.

Študenti, ki bodo uspešno končali ta predmet, bodo obvladali osnove inteligentnih sistemov in agentov in bodo usposobljeni za njihovo uporabo v reševanju zahtevnih problemov in vrednotenje njihovih rezultatov.

The goal of the course is to provide general and advanced knowledge of intelligent systems and intelligent agents in relation to artificial intelligence, ambient intelligence and information society. In the introduction, basic concepts, goals, motivations and objectives are presented.

The students who will successfully complete this course will master the basics of intelligent systems and agents and will be capable of applying these systems in solving demanding problems and evaluating their results.

Predvideni študijski rezultati:

Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- osnove znanstvenega pristopa in konceptov v umetni inteligenci, agentih in inteligentnih sistemih
- osnovna in napredna znanja umetne inteligence, agentov in inteligentnih sistemov
- pregled obstoječih nalog in metod
- obvladovanje tehničnih in praktičnih vidikov metod umetne inteligence in inteligentnih sistemov in agentov
- sposobnost uporabe obstoječih metod strojnega učenja in rudarjenja podatkov na novih problemih
- sposobnost ugotavljanja uspešnosti metod umetne inteligence, inteligentnih sistemov in agentov pri uporabi na konkretni nalogi

Students successfully completing this course will acquire:
- Basic scientific approach and concepts in artificial intelligence, agents and intelligent systems
- Basic and advanced knowledge about AI and intelligent systems and agents
- Overview of existing tasks and methods
- Mastering technical and practical aspects of AI, intelligent systems and agents
- The ability to apply existing ML and DM methods to new problems
- The ability to identify whether ML or DM methods are successful when used on a given domain

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo

Lectures, seminar, consultancy, individual work

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Seminarska naloga

Seminar work

Ustni zagovor

Oral defense

Reference nosilca / Lecturer's references:

1.	M. Gams, T. Kolenik. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules, Electronics 2021, 10(4), MDPI, DOI 10.3390/electronics10040514
2.	M. Shulajkovska, M. Smerkol, E. Dovgan, M. Gams. A machine-learning approach to a mobility policy proposal, Heliyon 9, 2023, DOI 10.1016/j.heliyon.2023.e20393
3.	M. Gams, Ž. Kolar, Z. Vuk. C. Samuelsson, B. Jäger, E. Dovgan. Similarities and Differences between EU Platforms in the AHA and AAL Domains from a Software Viewpoint. DOI, Healthcare 2022, 10(2). 10.3390/healthcare10020401,
4.	V. Janko, A. Vodopija, D. Susič, C. De Masi, T. Tušar, A. Gradišek, S. Vandepitte, D. De Smedt, J. Javornik, M. Gams, M. Luštrek. Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence, Frontiers 2023. C. 11, DOI 10.3389/fpubh.2023.1073581.
5.	M. Gjoreski, A. Gradišek, B. Budna, M. Gams, G. Poglajen. Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds. IEEE access, ISSN 2169-3536, 2020, vol. 8, str. 20313-20324, doi: 10.1109/ACCESS.2020.2968900.