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: Wrap up and summary exam - zeroeth term

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.


There are three projects to choose from:

  1. fox-geese-corn — simple planning problem.
  2. gangs-wars — problem about optimal ordering of tasks. Quite simple, but it's very difficult to find the optimal solution.
  3. production-planning — simplified problem of scheduling production at the factory.

All the project are available via gitlab. Instructions, how to do the projects are included in the files.

The deadline is simply last class in the semester. While grading I will check:

  1. if the model is correct;
  2. if the model allows to quickly find a good solution;
  3. if the model is comprehensible;
  4. what was your work hygiene (how often did you commit, did you contact in case of a problem, etc.)
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