====== 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: CSP (MSL)
  - 03-05: CSP (MSL)
  - 03-12: CSP (MSL)
  - 03-19: CSP (MSL)
  - 03-26: KRR introduction (GJN)
  - 04-02: LPP (GJN)
  - 04-09: Planning (MSL)
  - 04-16: Problog (MSL)
  - 05-07: KR methods overview (GJN)
  - 05-14: RBS (GJN)
  - 05-21: LOD (GJN)
  - 05-28: DL (GJN)
  - 06-04: Wrap up and summary <del>exam - zeroeth term</del>

==== Useful Links ====
  * [[http://www.hakank.org/|Hakank Links]]
  * [[http://www.minizinc.org/|MiniZinc]]
  * [[http://www.hakank.org/minizinc/|Hakank on MiniZinc]]
  * [[http://www.swi-prolog.org/|SWI-Prolog]]
  * [[http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html|Bayes Networks]]
  * [[https://dtai.cs.kuleuven.be/problog/|Problog]]
  * [[https://potassco.org/|ASP: Answer Set Programming]]
  * [[http://videolectures.net/acai05_berthold_fl/|Fuzzy Logic]]
  * [[http://www.francky.me/doc/course/fuzzy_logic.pdf|Introduction to Fuzzy Logic]]
  * [[http://dl.kr.org/courses.html|Description Logics]]

==== Background Material ====

   * [[http://artint.info/|AI Book]]
  *  [[http://ai.ia.agh.edu.pl/wiki/pl:prolog:start|Prolog - page with external links]]
  * [[http://web.stanford.edu/class/cs221/|CS221: Artificial Intelligence]]
  * [[http://web.stanford.edu/class/cs227/|CS227: Knowledge Representation and Reasoning]]
  * [[http://www.inzynieriawiedzy.pl/|KRR: PL]]

===== 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.  

  * Lab 1. [[en:dydaktyka:csp:intro|Constraint Programming: 101]]
  * Lab 2.[[en:dydaktyka:csp:lab1|Constraint Programming: Basic Problems]]
  * Lab 3. [[en:dydaktyka:csp:lab2|Constraint Programming: Basic Techniques]]
  * Lab 4. [[en:dydaktyka:csp:lab3|Constraint Programming: Search Modeling]]
  * Labs 5-6. [[en:dydaktyka:csp:port_scheduling|Constraint Programming: Real Life Problem]]

== Automated Planning ==

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

  - [[en:dydaktyka:planning:intro|Automated Planning: 101]]
  - [[en:dydaktyka:planning:pddl|Automated Planning: PDDL]]
  - [[en:dydaktyka:planning:pddl2|Automated Planning: Fluents]]

== Probabilistic Programming ==

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

  - [[en:dydaktyka:problog:intro|Probabilistic Programming: 101]]
  - [[en:dydaktyka:problog:lab1|Probabilistic Programming: Diagnosis and Prediction]]
  - [[en:dydaktyka:problog:lab2|Probabilistic Programming: Probabilistic Graphs and Decision Theory]]

==== Projects ====

There are three projects to choose from:
  - fox-geese-corn --- simple planning problem. 
  - gangs-wars --- problem about optimal ordering of tasks. Quite simple, but it's very difficult to find the optimal solution. 
  - production-planning --- simplified problem of scheduling production at the factory.

All the project are available via [[https://gitlab.com/agh-krr/2018-2019|gitlab]].
Instructions, how to do the projects are included in the ''README.md'' files.

The deadline is simply last class in the semester.
While grading I will check:
  - if the model is correct;
  - if the model allows to quickly find a good solution;
  - if the model is comprehensible;
  - what was your work hygiene (how often did you commit, did you contact in case of a problem, etc.)