IV126 Artificial Intelligence II

Faculty of Informatics
Autumn 2020
Extent and Intensity
2/0/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught online.
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 8:00–9:50 A318
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 lecture 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 course completes comprehensive introductory knowledge of artificial intelligence following the lecture PB016 Artificial Intelligence I. It presents additional important chapters from the classic book by Russell & Norvig Artificial Intelligence: A Modern Approach (see aima.cs.berkeley.edu). Local search, planning, dealing with uncertainty, and robotics are introduced in the course.
The graduate will be aware of base and advanced local search algorithms and will be able to solve practical problems with their help.
The graduate will be able to work with planning problems completed by a sequence of actions to achieve the given goal.
The graduate will gain an overview of how to work with uncertainties in the given problem and will learn to use basic procedures to include uncertainty in problem solving.
The graduate will be aware of the base concepts from robotics and will get an understanding of the robot path planning.
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 a NORVIG, Peter. Artificial intelligence : a modern approach (third edition). Prentice Hall, 2010.
  • 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
Video and slides for each lecture, videoconference during lecture time with discussion about questions sent in advance as well as other questions. Two homeworks during the semester.
Assessment methods
Evaluation is completed probably based on the online final exam (80 points) and two homeworks with practical examples solved during the semester (10 points per each homework), and bonus points for activity during videoconferences (about 24 points based on the number of lectures). The regular date of the exam will be written and mandatory for all students. Repair exam dates will be in the form of an oral online exam. Successful completion of the course requires getting more than 40 points for the online written exam at least and 8 points for homeworks at least. Also, each student can get 2 bonus points 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; 2 points for larger interaction). 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 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (Autumn 2020, recent)
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