IV126 Artificial Intelligence II

Faculty of Informatics
Autumn 2021
Extent and Intensity
2/0/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. Mgr. Hana Rudová, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Hana Rudová, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics
Timetable
Fri 17. 9. to Fri 17. 12. Fri 10:00–11:50 A217
Prerequisites
The course is a continuation of the PB016 Artificial Intelligence I, PB016 completion is not a prerequisite for course completion.
It is presumed knowledge of probability theory corresponding to the course MB103 Continuous models and statistics.
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 39 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, domain-specific planning. Plan space planning, partial order planning.
  • 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, 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. xvii, 1115. ISBN 9780134610993. 2021. 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, no drills, two homeworks. Lectures include exercises.
Assessment methods
Evaluation is completed based on the final written exam (80 points) and two homeworks with practical examples solved during the semester (10 points per homework), and bonus points for activity during lectures (about 12 points based on the number of lectures). Successful completion of the course requires getting more than 40 points for the written exam at least and 8 points for homeworks at least. 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 59-50.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
http://www.fi.muni.cz/~hanka/ai
The course is also listed under the following terms Spring 2015, Spring 2017, Spring 2018, Spring 2019, Autumn 2019, Autumn 2020, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (Autumn 2021, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2021/IV126