Knowledge Representation and Reasoning 2016/2017

Systems Modelling and Data Analysis


  1. Introduction to Knowledge Representation and Reasoning. Abduction. Basic Introduction to Constraint Programming [1.03.2017; ALi]
  2. Constraint Programming Tools. Introduction to MiniZinc. Building a Model: Einstein Puzzle [8.03.2017; ALi]
  3. Constraint Programming Tools. Introduction to MiniZinc. Simple example models. [15.03.2017; ALi]
  4. Constraint Programming Tools. Introduction to MiniZinc. Sets and arrays. Aggregation functions. Logical constraints. Production planning example. Job-Shop example. [22.03.2017; ALi]
  5. MiniZinc: selected predicates and application examples. Introduction to Constraint Propagation. [29.03.2017; ALi]
  6. Introduction to Knowledge Representation. The role of logic. Rule-Based Systems. [5.04.2017;ALi]
  7. E-Learning: Logic Programming and Rules: CS227: 8a, 8b; 9a, 9b [12.04.2017]
  8. E-Learning: Fuzzy Sets, Fuzzy Logic, Fuzzy Rules. [19.04.2017]
  9. Planning. Situation Calculus. STRIPS. Block World Examples. [26.04.2017; ALi]
  10. Advanced Planning. Hierarchical Planning. AND/OR Graphs search. Decomposition. Discrete-Even Systems. [10.05.2017]
  11. E-Learning: Causal Networks and Probabilistic Models. Bayes Networks. [17.05.2017]
  12. E-Learning: Problog: Probabilistic Logic Programming Models. [24.05.2017]
  13. E-Learning: ASP - Answer Set Programming. [31.05.2017]
  14. Recapitulation: Knowledge Representation and Reasoning. Test Problems. Exam 0. [7.06.2017]
  15. E-Learning: Description Logics. [14.06.2017]

Attention: new location: Building C-3, Room 101. Time: Wednesdays, 12:30-14:00

Lecture Slides
Background Material
New: Preparation for Exam

There is some background material indication: Exam focus

Permanently under construction


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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