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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
02-26:
03-05:
03-12:
03-19:
03-26:
04-02:
04-09:
04-16:
05-07:
05-14:
05-21:
05-28:
06-04:
06-11:
CSP (MSL) x4
Planning (MSL)
Problog (MSL)
KRR introduction (GJN)
LPP (GJN)
KR methods overview (GJN)
RBS (GJN)
LOD (GJN)
DL (GJN)
exam - zeroeth term
Useful Links
Background Material
Laboratories
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.
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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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Projects