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Reasoning tools and methods

General data

Course ID:	1000-2M16NMW
Erasmus code / ISCED:	11.3 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Reasoning tools and methods
Name in Polish:	Narzędzia wnioskowania
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science and Machine Learning
ECTS credit allocation (and other scores):	6.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Short description:	The course is devoted to practical aspects of reasoning. It will mainly concentrate around reasoning tools (implemented and publicly available) as well as methods necessary to successfully use these tools.
Full description:	1) Datalog: a) designing Datalog databases, b) query evaluation, negation, stratification, well-founded semantics, c) fuzzy rules, d) hypothetical reasoning, e) implementations: DES, XSB. 2) Answer Set Programming (ASP): a) introduction to the methodology and semantics of ASP, b) practical aspects of ASP, c) optimization problems, d) formulation of selected problems in ASP, e) ASP in knowledge representation, f) Implementation: Potassco. 3) Programming with constraints (CLP): a) introduction to methodology and semantics of CLP, b) formulation of selected problems in CLP, c) implementations: ECLiPSe CLP, CLP(R). 4) Other: a) Semantic Web, description logics (Protégé, Jena), b) probabilistic programming (Problog), c) reasoning in first-order logic (Vampire).
Bibliography:	Datalog: 1. S. Abiteboul, R. Hull, V. Vianu: Foundations of Databases, Addison-Wesley Pub. Co., 1996. 2. F. Saenz-Perez: Datalog Educational System V5.0. User’s Manual, Universidad Complutense de Madrid, 2017. ASP: 3. M. Gebser, R. Kaminski, B. Kaufmann, T. Schaub: Answer Set Solving in Practice, Morgan & Claypool Publishers, 2012. 4. M. Gelfond, Y. Kahl: Knowledge Representation, Reasoning, and the Design of Intelligent Agents. The Answer Set Programming Approach, Cambridge University Press, 2014. CLP: 5. K.R. Apt, M. Wallace: Constraint Logic Programming using ECLiPSe Prolog, Cambridge University Press, 2007. 6. A. Niederliński: A Gentle Guide to Constraint Logic Programming via ECLiPSe, PKJS Gliwice, 2014, http://www.anclp.pl/.
Learning outcomes:	1. Knowledge a. Has firm theoretical knowledge concerning complexity, deductive databases, software engineering used in intelligent systems (K_W02). b. Has knowledge about information management, including deductive databases, logical data modeling and information retrieval (K_W08). c. Knows logical methods of defining semantics of programs together with their mathematical foundations and practical techniques as well as correctness of programs and techniques and formalisms of proving correctness (K_W13). 2. Skills a. Ability to apply mathematical knowledge in formulating, analyzing and solving tasks of medium difficulty level (K_U01). b. Ability to fid information from the literature, knowledge bases, Internet, and other reliable sources as well as integrate, interpret them, derive conclusions and formulate opinions (K_U02). c. Ability to formulate database queries in selected query languages (K_U19). 3. Competences a. Understanding of limitations of own knowledge and the need for further studies, including knowledge from other areas (K_K01) b. Ability to search for relevant information in literature, also in foreign languages (K_K04).
Assessment methods and assessment criteria:	Final grade based on implemented projects, exercises solved during labs.

Classes in period "Winter semester 2024/25" (past)

Time span:	2024-10-01 - 2025-01-26	Selected timetable range: weekly course term Go to timetable MO WYK CW TU W TH FR
Type of class:	Classes, 30 hours more information Lecture, 30 hours more information
Coordinators:	Anh Linh Nguyen
Group instructors:	Anh Linh Nguyen
Students list:	(inaccessible to you)
Credit:	Examination

Course descriptions are protected by copyright.
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