IV126 Fundamentals of Artificial Intelligence

Faculty of Informatics
Autumn 2024
Extent and Intensity
2/0/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Mgr. Hana Rudová, Ph.D. (lecturer)
Mgr. Václav Sobotka (assistant)
Guaranteed by
doc. Mgr. Hana Rudová, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Wed 25. 9. to Wed 18. 12. Wed 16:00–17:50 A217
Prerequisites
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
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 36 fields of study the course is directly associated with, display
Course objectives
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.
Learning outcomes
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.
Syllabus
  • 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.
Literature
  • RUSSELL, Stuart J. and 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 and Paolo TRAVERSO. Automated Planning: Theory & Practice. Morgan Kaufmann, 2004. info
Teaching methods
Standard lecture, one homework, and one written test during the semester. Lectures include exercises. Slides in Czech will be available from past semesters.
Assessment methods
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.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/el/fi/podzim2024/IV126/index.qwarp
Authenticated access see https://is.muni.cz/auth/el/fi/podzim2024/IV126/index.qwarp
The course is also listed under the following terms Spring 2015, Spring 2017, Spring 2018, Spring 2019, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023.
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