Artificial Intelligence Algorithms (FSI-VAI-A)

Academic year 2018/2019
Supervisor: prof. Ing. Radomil Matoušek, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: English
Aims of the course unit:
The course objective is to make students familiar with basic resources of artificial intelligence, potential and adequacy of their use in engineering problems solving.
Learning outcomes and competences:
Understanding of basic methods of artificial intelligence and ability of their implementation.
Prerequisites:
The knowledge of basic relations of the graphs theory and object oriented technologies is expected.
Course contents:
The course introduces basic approaches to artificial intelligence algorithms and classical methods used in the field. Main emphasis is given to automated formulas proves, knowledge representation and problem solving. Practical use of the methods is demonstrated on solving simple engineering problems.
Teaching methods and criteria:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:
Course-unit credit requirements: submitting a functional software project which uses implementation of selected AI method. Project is specified in the first seminar. Systematic checks and consultations are performed during the semester. Each student has to get through one test and complete all given tasks. Student can obtain 100 marks, 40 marks during seminars (20 for project and 20 for test; he needs at least 20), 60 marks during exam (he needs at least 30).
Controlled participation in lessons:
The attendance at lectures is recommended, at seminars it is obligatory. Education runs according to week schedules. The form of compensation of missed seminars is fully in the competence of a tutor.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture 1. Introduction, AI areas.
2. Uninformed search in state space.
3. Informed search methods.
4. Knowledge representation by rules, production systems.
5. Evolutionary search methods.
6. Problem solving by decomposition into sub-problems, AND/OR search methods.
7. Game playing methods.
8. Knowledge representation by predicate logic formulas, resolution method.
9. Horn logic and Prolog. Non-traditional logics.
10. Knowledge representation by semantic networks, frames, scripts and objects.
11. Machine learning.
12. Intelligent and reactive agents.
13. Multiagent systems.
    Computer-assisted exercise 1. Uninformed methods of state space search - theoretical analysis.
2. Uninformed methods of state space search - implementation using object oriented programming.
3. Informed methods of state space search - gradient algorithm, Dijkstra’s algorithm, best-first search algorithm, theoretical analysis.
4. A-star algorithm - theoretical analysis, implementation using object oriented programming.
5. Solving problems by means of genetic algorithms.
6. Decomposition of a problem into sub-problems, AND-OR graph.
7. Object design and implementation of AND/OR graph.
8. Game playing methods, minimax, alpha-beta pruning.
9. Intermediate test.
10. Predicate logic formulas, resolution method.
11. Solving AI problems by means of Prolog.
12. Solving a selected practical problem by means of AI.
13. Presentation of semester projects.
Literature - fundamental:
1. Russel, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education 2021.
2. Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Pearson Education 2011.
3. Bratko, I. Prolog Programming for Artificial Intelligence. Pearson Education Canada 2011.
Literature - recommended:
1. Russel, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall 2010. https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf
2. Poole, D.L. and Mackworth, A.K. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press 2023. https://artint.info/3e/html/ArtInt3e.html
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2I-A full-time study M-AIŘ Applied Computer Science and Control -- Cr,Ex 5 Compulsory 2 1 S
M2I-Z visiting student M-STI Mechanical Engineering -- Cr,Ex 5 Recommended course 2 1 S