Infopaket

Cognitive modeling and knowledge engineering

ОК 10

Обов’язкова дисципліна для ОП

Second

2023/2024

2 Semester

5

Create new algorithms for solving problems in the field of computer science, evaluate their effectiveness and limitations on their application Design and maintain databases and knowledge. Test the software. Identify the needs of potential customers regarding the automation of information processing.

Knowledge of discrete mathematics, graph theory, requirements analysis methods for computer systems and software, decision making theory, database and knowledge bases. Possess elementary skills of discrete mathematics, graph theory, be able to analyze, synthesize and structure requirements for a computer system, apply mathematical methods of justification and decision-making, adequate to the conditions in which information and computer systems of various purposes function, be able to design databases and knowledge taking into account the requirements for the functioning of the object of computerization.

The course "Cognitive modeling and knowledge engineering" considers the concept of cognitive modeling, features of knowledge discovery from experience (empirical facts) and functions of a cognitologist (knowledge engineer), construction of cognitive maps and their use for analysis, prediction of behavior and management of complex systems, cognitive structuring of knowledge . The goal of the discipline is to provide students with the knowledge and skills necessary to apply the methods and tools of cognitive modeling and knowledge engineering in the tasks of analysis, behavior prediction and management of complex systems..

Bolton, M.L. & Gray, Wayne. (2023). Cognitive Modeling for Cognitive Engineering. 10.1017/9781108755610.038. https://www.researchgate.net/publication/370681449_Cognitive_Modeling_for_Cognitive_Engineering Knox, Carissa & Gray, Steven & Zareei, Mahdi & Wentworth, Chelsea & Aminpour, Payam & Wallace, Renee & Hodbod, Jennifer & Brugnone, Nathan. (2023). Modeling complex problems by harnessing the collective intelligence of local experts: New approaches in fuzzy cognitive mapping. Collective Intelligence. 2. 10.1177/26339137231203582. https://www.researchgate.net/publication/374897602_Modeling_complex_problems_by_harnessing_the_collective_intelligence_of_local_experts_New_approaches_in_fuzzy_cognitive_mapping Guan, Maime & Stokes, Ryan & Vandekerckhove, Joachim & Lee, Michael. (2023). A cognitive modeling analysis of risk in sequential choice tasks. Judgment and Decision Making. 15. 823-850. 10.1017/S1930297500007956.

Lectures, laboratory activities, individual work

During the semester, students perform laboratory work and present the results of their performance in front of the audience. The defense of works is carried out in the format of answers to the teacher's questions and discussions with the audience. Laboratory works are evaluated with weighted points according to the complexity of the works. The maximum number of points that a student can receive for work during the semester is 60 points on a 100-point scale. The final assessment is an exam in the format of a computer test, which includes theoretical and practical questions. The total score for the exam is 40 points on a 100-point scale. If a student gets less than 24 points during the exam, he is considered "unsatisfactory" and the points scored are not counted. The recommended minimum for admission to the exam is 36 points, the critical calculated minimum is 20 points. In order to be admitted to the exam, it is mandatory to complete at least 50% of all laboratory work.

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