B-KUL-H02C3A Knowledge Representation

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General information

Academic year: 2011-2012 Study points: 4 Language: English Difficulty: Advanced Duration: 28.0 hours  Periodicity: Taught in the second semester POC: POC Artificial Intelligence This course cannot be followed within the context of an exam contract

Print version

Taught by

Denecker Marc
Fierens Daan (substitute)

Aims

• Conveying insight in the process of knowledge representation and its role in AI and Computer Science.
• Conveying insight in knowledge representation formalisms, their differences and correspondences
• Conveying insight in different types  of knowledge and the methodology to express them.
• Developing  skills in expressing knowledge and solving computational tasks.
• Getting in touch with current hot research topics and questions about knowledge representation languages and tools:
•  important research topics
• important open research questions
• experimentation with  state-of-the-art inference tools.

Previous knowledge

Basics of Artificial Intelligence.

Content

• Introduction on the role of Knowledge Representation  in AI
  (Informal introduction to different types of knowledge and propositional attitudes, possible world analysis of knowledge, the role of knowledge in problem solving, the controversies and trade-offs of KR in AI)
•  Knowledge representation in classical logic
  (Syntax, informal and formal semantics of classical logic, KR methology in classical logic)
• Extending classical logic with definitions
  (introduction to different types of definitions and inductive definitions, syntax and formal semantics of ID-logic)
• Using classical logic for problem solving
  (The role of different forms of logical inference in AI and for problem solving)
•  Epistemic modal logic
  (Reasoning about knowledge, beliefs and intentions of other agents, syntax, formal and informal semantics of modal logic, correspondence theory, application to multi-agents systems)
  
• Knowledge representation in probabilistic logics
  (Introduction to probabilistic logics. Case study of CP-logic: syntax, informal and formal semantics.)
  
• Introduction to non-logical  KR-formalisms
  (Production rules and frame-based systems)

This course is included in

Master of Science in Artificial Intelligence   (Option Engineering and Computer Science (ECS)) (Option Cognitive Science (CS))
Master of Science in de ingenieurswetenschappen: computerwetenschappen (geen nieuwe inschrijvingen in 2011-2012)   (Artificiële intelligentie)
Master of Science in Artificial Intelligence   (Option Speech and Language Technology (SLT))
Master of Science in de ingenieurswetenschappen: computerwetenschappen (nieuw programma, start in 2010)   (Hoofdspecialisatie Artificiële intelligentie)
Master of Science in de toegepaste informatica   (Artificiële intelligentie en gegevensbanken)

Course Material

Text book
Articles and literature
Slides, transparencies, courseware
Toledo / e-platform

Activities

		B-KUL-H00G7a Knowledge Representation: Exercises
	General information Study points: 0.50 Language: English Category: Exercises Duration: 8.5 hours  Periodicity: Taught in the second semester POC: POC Artificial Intelligence Taught by Denecker Marc Fierens Daan (substitute) Content The excercise sessions address the following topics: KR methodology using classical logic Syntax , semantics and KR of definitional knowledge Use of inference tools for automated problem solving using declarative specifications Modal and multi-modal logics, Kripke structures Probabilistic logics, syntax and semantics of CP-logic Course Material Exercises and model solutions are made available on Toledo Students use the state-of-the-art inference system IDP to evaluate  the correctness of their solutions for KR excercises The system is also used to show the role of inference for automated problem solving using declarative specifications.
		B-KUL-H02C3a Knowledge Representation: Lecture
	General information Study points: 3.50 Language: English Category: Lectures Duration: 19.5 hours  Periodicity: Taught in the second semester POC: POC Artificial Intelligence Taught by Denecker Marc Fierens Daan (substitute) Course Material Slides on Toledo Book "Knowledge Representation and Reasoning" Ronald Brachman and Hector Levesque

Evaluation

		B-KUL-H22C3a Evaluation : Knowledge Representation
	Evaluation description Explanation Written exam (3h) :  Theoretical part (10pt): closed book  Exercise part(10pt): open book   June and SeptemberNo projects for this course