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Course, academic year 2017/2018
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Artificial Intelligence I - NAIL069
Czech title: Umělá inteligence I
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2015
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/1 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: English, Czech
Teaching methods: full-time
Additional information: http://ktiml.mff.cuni.cz/~bartak/ui/
Guarantor: prof. RNDr. Roman Barták, Ph.D.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
Annotation -
Last update: prof. RNDr. Roman Barták, Ph.D. (10.05.2011)

An introductory course on artificial intelligence with the focus on basic concepts and methods. The cources requires knowledge of logic at the level of undergraduate course.
Aim of the course -
Last update: BARTAK/MFF.CUNI.CZ (31.03.2008)

The course gives an introduction to fundamental concepts and techniques of Artificial Intelligence. The students will learn several search techniques for problem solving, including informed search such as A* algorithm, methods of logical representation of knowledge, inference techniques, constraint satisfaction, and planning techniques.

Literature - Czech
Last update: BARTAK/MFF.CUNI.CZ (18.02.2008)

S. Russell, P. Norvig: Artificial Intelligence; A Modern Approach, 2003

V. Mařík, O. Štepánková, J. Lažanský a kol.: Umělá Inteligence (1). Academia, Praha

V. Mařík, O. Štepánková, J. Lažanský a kol.: Umělá Inteligence (2). Academia, Praha

Z. Renc: Vybrané partie z umělé inteligence. Skriptum MFF UK Praha, 1987

Teaching methods -
Last update: BARTAK/MFF.CUNI.CZ (31.03.2008)

lecture

Syllabus -
Last update: BARTAK/MFF.CUNI.CZ (18.02.2008)

Intelligent agents, environment, and structure of agents.

Problem solving by search (DFS, BFS, ID, A*, IDA*, local and on-line search, heuristics).

Constraint satisfaction.

Games (minimax, alfa-beta pruning).

Knowledge representation and inference techniques (forward and backward chaining, resolution).

Automated planning.

 
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