Магистратура 2021/2022

Введение в представление знаний

Статус: Курс по выбору (Науки о данных (Data Science))

Направление: 01.04.02. Прикладная математика и информатика

Кто читает: Кафедра технологий моделирования сложных систем

Где читается: Факультет компьютерных наук

Когда читается: 2-й курс, 1, 2 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Рыгаев Иван Петрович

Прогр. обучения: Науки о данных

Язык: английский

Кредиты: 8

Контактные часы: 54

Course Syllabus

Abstract

You may have heard that the Turing Test was formally passed by a chat-bot named Eugene Gustman in 2014. He fooled one-third of judges that he is a person. But does the chat-bot think? Many believe it does not. It is just a clever piece of software that uses verbal acrobatics and trickery to hide the fact that it does not understand a word. But what is understanding? How can we measure it? Another test called Winograd Schema Challenge was proposed in 2011, which does not require conversation at all. It is technically much simpler and is based on the number of multiple-choice questions of a special form. Here are a couple of examples: 1. The trophy doesn't fit into the brown suitcase because it's too small. What is too small, the suitcase or the trophy? 2. Frank felt vindicated when his longtime rival Bill revealed that he was the winner of the competition. Who was the winner of the competition, Frank or Bill? These are very simple questions for humans but computers have a hard time answering them. A computer would need to possess so-called commonsense knowledge and perform commonsense reasoning to pass the test. In this course, we will explore how such kind of knowledge can be represented in formal systems and how reasoning is performed in such systems.

Learning Objectives

• Gain theoretical knowledge and practical skills of knowledge representation, learn popular standards and tools for knowledge representation and reasoning

Expected Learning Outcomes

• Abilitty to express logical propositions in the form of existential graphs and to use graphical rules of reasoning to draw inferences
• Ability to express statements in terms of propositional and first-order logic and use natural deduction for proofs
• Basic understanding of paraconsistent logics, intiuitionistic logic and relevant logic
• Knwoledge of ideas behind Datalog, its limitations and extensions. Ability to use Datalog reasoners (such as DLV).
• Understanding of basic principles of higher-order logic and modal logic, predicate quantification and possible worlds semantics
• Understanding of Description Logic and OWL. Ability to create OWL ontologies in Protege.
• Understanding of different parts of the meaning of a sentence: assertion, presupposition, implicature, entailment
• Understanding of principles of construction of semantic networks and conceptual graphs. Ability to represent knowledge using these formalisms.
• Understanding of resolution method of automated reasoning. Ability to encode the problem in Prover9 and derive the proof.
• Understanding of Semantic Web standards: RDF, Turtle, SPARQL, triplestore. Ability to query data from open knowledge bases (dbpedia, wikidata and so on)
• Unserstanding of backward chaining algorithm used in Prolog. Ability to write declarative programs in Prolog.

Course Contents

• Classical logic
• Existential graphs
• Logic and language
• Higher-order logic and modal logic
• Automated theorem provers
• Prolog
• Datalog
• Semantic networks and conceptual graphs
• Semantic Web, RDF, Turtle, SPARQL
• Description Logic and OWL ontologies
• Intuitionistic and relevant logic

Assessment Elements

• Homework assignment 1
• Homework assignment 2
• Homework assignment 3
• Homework assignment 4

Interim Assessment

• 2021/2022 2nd module
  0.25 * Homework assignment 2 + 0.25 * Homework assignment 3 + 0.25 * Homework assignment 4 + 0.25 * Homework assignment 1

Bibliography

Recommended Core Bibliography

• A course in mathematical logic for mathematicians, Manin, Y. I., 2010
• Automation of reasoning, , 1983
• Datalog in academia and industry : 2nd International Workshop, Datalog 2.0, Vienna, Austria, September 11-13, 2012: proceedings, , 2012
• Foundations of semantic Web technologies, Hitzler, P., 2010
• Graph-based knowledge representation : computational foundations of conceptual graphs, Chein, M., 2009
• Handbook of logic and language, , 2011
• Intuitionism, Kaspar, D., 2012
• Modal logic : an introduction to its syntax and semantics, Cocchiarella, N. B., 2008
• The description logic handbook : theory, implementation, and applications, , 2010
• The essential Peirce. Vol.2: 1893-1913, Peirce, C.S., 1998
• Логическое программирование на языке Visual Prolog : учеб. пособие для вузов, Цуканова, Н. И., 2008

Recommended Additional Bibliography

• A concise introduction to logic, Hurley, P.J., 2017
• Programming the Semantic Web, Segaran, T., 2009
• Programming with higher - order logic, Miller, D., 2012
• The essential Peirce. Vol.1: 1867-1893, Peirce, C.S., 1992
• Theory and applications of ontology: computer applications, , 2010
• Theory and applications on ontology: philosophical perspectives, , 2010
• Алгоритмы искусственного интеллекта на языке PROLOG, Братко, И., Птицина, К. А., 2004