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:

Jeziki / Languages:

Predavanja / Lectures:

Slovenščina, angleščina / Slovenian, English

Vaje / Tutorial:

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

Prerequisits:

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).

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).

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:

Cilji in kompetence:

Objectives and competences:

Predvideni študijski rezultati:

Intendeded learning outcomes:

Metode poučevanja in učenja:

Learning and teaching methods:

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Seminarska naloga

Seminar work

Ustni zagovor

Oral defense

Reference nosilca / Lecturer's references:

1.	A. Tavčar, D. Kužnar, and M. Gams, “Hybrid multi-agent strategy discovering algorithm for human behavior”. Expert systems with applications, ISSN 0957-4174, vol. 71, pp. 370-382, 2017.
2.	M. Gjoreski, H. Gjoreski, M. Luštrek, and M. Gams. “How accurately can your wrist device recognize daily activities and detect falls?”. Sensors, ISSN 1424-8220, vol. 16, no. 6, pp. 800-1-800-21, 2016.
3.	H. Gjoreski, B. Kaluža, M. Gams, R. Milić, and M. Luštrek. “Context-based ensemble method for human energy expenditure estimation.” Applied soft computing, ISSN 1568-4946, vol. 37, pp. 960-970, 2015.
4.	H. Gjoreski, S. Kozina, M.Gams, M. Luštrek, J.A. Álvarez-García, J.H. Hong, J. Ramos, A.K. Dey, M. Bocca, and N. Patwari. “Competitive live evaluations of activity-recognition systems.” IEEE pervasive computing, vol. 14, no. 1, pp. 70-77, 2015.
5.	V. Vidulin, M. Bohanec, and M. Gams. “Combining human analysis and machine data mining to obtain credible data relations.” Information sciences, vol. 288, pp. 254-278, 2014.