Introduction to artificial intelligence

Introduction to Artificial Intelligence, examples of applications
State space and basic search algorithms: depth-first, breadth-first and iterative deepening, complexity of these algorithms
Heuristic search, algorithms A and IDA, admissibility theorem for A, properties of heuristic function and analysis of time and space complexity
Problem decomposition with AND/OR graphs, search in AND/OR graphs, heuristic search algorithm AO
Machine learning: problem of learning from data, data mining, description languages and hypothesis spaces, induction of decision trees, regression trees, model trees, and rules. Software tools for machine learning and applications.
Knowledge representation and expert systems: knowledge representation with rules, frames, semantic networks, ontologies, inference algorithms and generationg explanation, handling uncertain knowledge, Bayesian networks
Means-ends planning, total-order and partial-order planning, goal regression, applications in robotics and logistics

I. Bratko, Prolog Programming for Artificial Intelligence, 4th edition, Pearson Education,
Addison-Wesley 2011, ISBN: 0201403757.
S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Third edition, Pearson
Education, Prentice-Hall 2010, ISBN: 0136042597.
I. Bratko, Prolog in umetna inteligenca, Založba FE in FRI, ponatis 2011.
I. Kononenko, Strojno učenje, Založba FE in FRI, 2005.
Materiali na spletu (Spletna učilnica FRI, Ivan Bratko home page): Prosojnice predavanj, naloge.

Teach basic concepts, ideas, methods and techniques of artificial intelligence (AI)
Ability to solve problems with methods of artificial intelligence
Ability to understand the literature in the area of AI
Contribute to the understanding of the relevance of technical achievements of AI with respect to their implications in philosophy and psychology

Knowledge and understanding:
The student recognises and understands the most frequently applied techniques of AI
Application:
The students is capable of applying methods of AI in the planning and development of concrete computer applications in various application areas
Reflection:
The student is capable of judging the implications of technical achievements of AI regarding the possibilities and limitations in computer applications, the limits of computer intelligence, similarities and differences with human intelligence, and some questions of cognitive science.
Transferable skills:
Skills are not limited to one subject; the student is capable of applying the learned methods in the development of computer applications and systems in general.

Lectures, laboratory exercises, homework, individual and team projects

Continuing (homework, midterm exams, project work)
Final (written and oral exam)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

BRATKO, Ivan. Prolog programming for artificial intelligence. 4th ed. Harlow (England) [etc.]: Addison-Wesley: Pearson, cop. 2012. XXI, 673 str., ilustr. ISBN 978-0-321-41746-6. ISBN 0-321-41746-1. [COBISS-SI-ID 8577364]
MOŽINA, Martin, ŽABKAR, Jure, BRATKO, Ivan. Argument based machine learning. Artificial intelligence, ISSN 0004-3702. [Print ed.], 2007, vol. 171, no. 10/15, str. 922-937. [COBISS-SI-ID 6240084]
LUŠTREK, Mitja, GAMS, Matjaž, BRATKO, Ivan. Is real-valued minimax pathological?. Artificial intelligence, ISSN 0004-3702. [Print ed.], 2006, vol. 170, str. 620-642. [COBISS-SI-ID 19805735]
ŠUC, Dorian, VLADUŠIČ, Daniel, BRATKO, Ivan. Qualitatively faithful quantitative prediction. Artificial intelligence, ISSN 0004-3702. [Print ed.], 2004, vol. 158, no. 2, str. [189]-214, ilustr. [COBISS-SI-ID 4422740]
BRATKO, Ivan, MOZETIČ, Igor, LAVRAČ, Nada. Kardio : a study in deep and qualitative knowledge for expert systems. Cambridge (Mass.), London: The MIT Press, 1989. XIV, 260 str., graf. prikazi. ISBN 0-262-02273-7. [COBISS-SI-ID 19925760]