IV126 Artificial Intelligence II

Faculty of Informatics
Spring 2018
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Hana Rudová, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics
Timetable
Thu 14:00–15:50 C525
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
Course objectives
Course provides information from selected areas of artificial intelligence about search algorithms concentrating on metaheuristics and local search, about classical planning in artificial intelligence, about uncertain reasoning and basic introduction about robotics.
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.
Graduate will be aware of base and advanced local search algorithms and will be able to solve practical problems with their help.
Graduate will be able to work with planning problems completed by sequence of actions to achieve given goal.
Graduate will be aware of the base concepts from robotics, will be aware of robot perceptions and planning movements.
Syllabus
  • Local search and metaheuristics: Single-solution based search, principles and concepts, strategies for improving local search. Population-based search, evolutionary algorithms, swarm intelligence. Multi-objective optimization, Pareto optimum.
  • Planning: Problem representation. State space planning, forward and backward planning, STRIPS operators. Plan space planning, partial order planning.
  • Uncertain knowledge and reasoning: Probabilistic reasoning, Bayesian networks, exact and approximate inference. Probabilistic reasoning over time, time and uncertainty, Markov processes. Utility theory, decision networks, decision in time, Markov decision processes.
  • Robotics: Robot hardware, sensors, effectors. Robotic perception, localization and mapping. Planning to move, planning uncertain movements.
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
Standard lecture, no drills, two homeworks. Lectures include exercises.
Assessment methods
Evaluation is completed based on final written exam (80 points) and two homeworks with practical examples solved during the semester (10 points per each homework). Successful completion of the course requires to get 8 points for homeworks at least. Evaluation is A 100-90, 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 2019, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Spring 2018, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2018/IV126