- 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.
Basics of Artificial Intelligence.
- 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)
Articles and literature
Slides, transparencies, courseware
Toledo / e-platform
Is also included in other courses
- Master in de toegepaste informatica (Artificial Intelligence and Databases) 60 ects.
- Master of Artificial Intelligence 60 ects.
- Master in de ingenieurswetenschappen: computerwetenschappen (Specialisation: Artificial Intelligence) 120 ects.
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
- 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.
Written exam (3h) :
- Theoretical part (10pt): closed book
- Exercise part(10pt): open book
- June and SeptemberNo projects for this course