Subject description
- 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
The subject is taught in programs
Objectives and competences
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Teach basic concepts, ideas, methods and techniques of artificial intelligence (AI)
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Ability to solve problems with methods of artificial intelligence
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Ability to understand the literature in the area of AI
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Contribute to the understanding of the relevance of technical achievements of AI with respect to their implications in philosophy and psychology
Teaching and learning methods
Lectures, laboratory exercises, homework, individual and team projects
Expected study results
After the completion of the course the student will be able to:
– know the basic and most commonly used methods in the field of artificial intelligence (AI),
– explain implications of the AI achievements, and its relation with cognitive science, psychology, medicine, logic, mathematics and other related fields,
– define the technical boundaries of the field,
– use the search and machine learning algorithms on real problems,
– compare time and spatial complexity of the taught algorithms,
– formulate selected problems from the real world as problems that are solvable with the AI algorithms.
Basic sources and literature
. 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.
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