Systems Modelling and Data Analysis + Engineering of Intelligent Systems

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

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:

• Hakank Links
• MiniZinc
• Hakank on MiniZinc
• SWI-Prolog
• Bayes Networks
• Problog
• ASP: Answer Set Programming
• Fuzzy Logic
• Introduction to Fuzzy Logic
• Description Logics

• AI Book
• Prolog - page with external links
• CS221: Artificial Intelligence
• CS227: Knowledge Representation and Reasoning
• KRR: PL

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.

• Lab 1. Constraint Programming: 101
• Labs 2 and 3.Constraint Programming: Basic Problems
• Lab 4. Constraint Programming: Basic Techniques
• Lab 5. Constraint Programming: Search Modeling

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

1. Automated Planning: 101
2. Automated Planning: PDDL
3. Automated Planning: Fluents

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

1. Probabilistic Programming: 101
2. Probabilistic Programming: Diagnosis and Prediction
3. Probabilistic Programming: Probabilistic Graphs and Decision Theory