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Knowledge Representation and Reasoning

Systems Modelling and Data Analysis + Engineering of Intelligent Systems

Lectures: Tuesdays, C-2, Room 429, 15:30-17:00

Lectures 2019

  1. 02-26: CSP (MSL)
  2. 03-05: CSP (MSL)
  3. 03-12: CSP (MSL)
  4. 03-19: CSP (MSL)
  5. 03-26: KRR introduction (GJN)
  6. 04-02: LPP (GJN)
  7. 04-09: Planning (MSL)
  8. 04-16: Problog (MSL)
  9. 05-07: KR methods overview (GJN)
  10. 05-14: RBS (GJN)
  11. 05-21: LOD (GJN)
  12. 05-28: DL (GJN)
  13. 06-04: exam - zeroeth term
  14. 06-11:

Background Material


Constraint Satisfaction and Discrete Optimization

The following classes will focus on modelling of discrete optimization and constraint satisfaction problems. Student will learn how to represent correctly different problems using constraint programming techniques.

Automated Planning

The following classes will cover automated planning problems. Student will learn how to represent planning problems using constraint programming and dedicated tools.

Probabilistic Programming

This part of the course will present probabilistic programming — a new programming paradigm meant to model domains uncertainty and imperfect knowledge.



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