IV126 Fundamentals of Artificial Intelligence

Fakulta informatiky
podzim 2024
Rozsah
2/0/1. 3 kr. (plus ukončení). Ukončení: zk.
Vyučováno kontaktně
Vyučující
doc. Mgr. Hana Rudová, Ph.D. (přednášející)
Mgr. Václav Sobotka (pomocník)
Garance
doc. Mgr. Hana Rudová, Ph.D.
Katedra strojového učení a zpracování dat – Fakulta informatiky
Dodavatelské pracoviště: Katedra strojového učení a zpracování dat – Fakulta informatiky
Rozvrh
St 25. 9. až St 18. 12. St 16:00–17:50 A217
Předpoklady
The course is a continuation of the PB016 Introduction to Artificial Intelligence, PB016 completion is not a prerequisite for course completion.
It is presumed knowledge of graphs based on IB002 Algorithms and data structures I, and probability theory corresponding to the course MB153 Statistics I
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
předmět má 36 mateřských oborů, zobrazit
Cíle předmětu
The course completes comprehensive introductory knowledge of artificial intelligence following the course PB016 Artificial Intelligence I. The course discusses search algorithms concentrating on metaheuristics and local search, classical planning, uncertain reasoning, and introduction into robotics oriented on robot path planning.
Výstupy z učení
The graduate will be aware of local search and metaheuristics algorithms and will be able to solve practical problems with their help.
The graduate will understand problematics of the AI planning, will learn how to represent planning problem and how to solve it using base algorithms.
The graduate will gain an overview of how to work with uncertainties in the given problem and will learn to use basic procedures for including uncertainty in problem solving.
The graduate will be aware of the base concepts from robotics which is used for demonstration how the above knowledge can be applied, especially in the planning of robot motion.
Osnova
  • Local search and metaheuristics: Single-solution based search, principles, and concepts, strategies for improving local search. Population-based search, evolutionary algorithms, swarm intelligence.
  • Planning: Problem representation. State space planning, forward and backward planning, and domain-specific planning. Plan space planning and partial order planning. Hierarchical task networks.
  • Uncertain knowledge and reasoning: Probabilistic reasoning, Bayesian networks, exact and approximate inference. Time and uncertainty. Utility theory, decision networks. Sequential decision problems, Markov decision processes.
  • Robotics: Robot hardware, robotic perception, and robot scheduling in manufacturing. Path planning in robotics, movement.
Literatura
  • RUSSELL, Stuart J. a Peter NORVIG. Artificial intelligence : a modern approach. Fourth edition. Hoboken: Pearson, 2021, xvii, 1115. ISBN 9780134610993. info
  • TALBI, El-Ghazali. Metaheuristics: From Design to Implementation. Wiley, 2009.
  • GHALLAB, Malik, Dana NAU a Paolo TRAVERSO. Automated Planning: Theory & Practice. Morgan Kaufmann, 2004. info
Výukové metody
Standard lecture, one homework, and one written test during the semester. Lectures include exercises. Slides in Czech will be available from past semesters.
Metody hodnocení
Evaluation is completed based on the final written exam (70 points), one programming homework during the semester aimed to solve a practical problem (10 points), one written test during the semester (20 points), and bonus points for activity during lectures (about 12 points based on the number of lectures). Successful completion of the course requires getting at least 40 points for the written exam and at least 13 points for the work during the semester (homework, written test). Also, each student can get 1 bonus point for activity in each lecture (e.g., student response to several easy questions and/or student questions to clarify some part of the lecture or student response to one harder question). Evaluation is the following: A more than 90, B 89-80, C 79-70, D 69-60, E 59-55.
Vyučovací jazyk
Angličtina
Informace učitele
https://is.muni.cz/el/fi/podzim2024/IV126/index.qwarp
Authenticated access see https://is.muni.cz/auth/el/fi/podzim2024/IV126/index.qwarp
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Studijní materiály
Předmět je vyučován každoročně.
Nachází se v prerekvizitách jiných předmětů
Předmět je zařazen také v obdobích jaro 2015, jaro 2017, jaro 2018, jaro 2019, podzim 2019, podzim 2020, podzim 2021, podzim 2022, podzim 2023.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/fi/podzim2024/IV126