Potassco Slide Packages are licensed under a Creative Commons Attribution 3.0 Unported License.
Answer Set Programming, Course at University of Potsdam
This is a fullfledged lecture series over an entire semester. The teaching material follows our forthcoming book on Answer Set Solving in Practice
Note that the material is still expanding and subject to change.
All Slides LaTeX Bundle and PDF (current and changing; Gringo 3 version (G3))
 Motivation LaTeX PDF
 Introduction LaTeX PDF (G3: LaTeX PDF)
 Basic Modeling LaTeX Bundle PDF (G3: LaTeX Bundle PDF) Videos (One, Two)
 Language LaTeX Bundle PDF (G3: LaTeX Bundle PDF)
 Language Extensions LaTeX Bundle PDF (G3: LaTeX Bundle PDF)
 Grounding LaTeX PDF (preliminary)
 Computational Aspects LaTeX Bundle PDF
 Characterizations LaTeX Bundle PDF
 Solving LaTeX Bundle PDF
 Multshot Solving LaTeX Bundle PDF
 Potassco Systems LaTeX Bundle PDF (preliminary; no G3 specifics)
 Advanced Modeling LaTeX Bundle PDF (preliminary; G3: LaTeX Bundle PDF)
 Constraint Solving LaTeX PDF (preliminary)
 Preferences and Optimization LaTeX PDF
Slides (2011) Handout (2011) Moodle Documentation Support Videos
Towards Embedded Answer Set Solving, Tutorial at CP'15
The focus of this short tutorial lies on recent techniques needed for embedding ASP in complex software environments. The tutorial starts with a short introduction to the essential formal concepts of ASP, needed for understanding its semantics and solving technology. The remainder is dedicated to using ASP in conjunction with Python for modeling complex reasoning scenarios. This involves an introduction to the API of clingo 4, an ASP system extending clasp and gringo with control capacities expressible in Python (and Lua). We illustrate this by developing a sample board game and its more sophisticated usage in preference handling and optimization.
Towards Embedded Answer Set Solving, Tutorial at RW'15
The focus of this halfday tutorial lies on recent techniques needed for embedding ASP in complex software environments. The tutorial starts with an introduction to the essential formal concepts of ASP, needed for understanding its semantics and solving technology. The remainder is dedicated to using ASP in conjunction with Python for modeling complex reasoning scenarios. This involves an introduction to the API of clingo 4, an ASP system extending clasp and gringo with control capacities expressible in Python (and Lua). We illustrate this by developing a sample board game and its more sophisticated usage in preference handling and optimization.
Answer Set Solving in Practice: Advanced techniques, Tutorial at IJCAI'15
This fullday tutorial presents a practical introduction to Answer Set Programming (ASP), aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's modeling language and methodology, grounding and solving technology, and finally details (Pythonbased) control techniques needed for embedding ASP in complex software environments.
Slides Resources Documentation Support
Answer Set Solving in Practice, Tutorial at IJCAI'13
This halfday tutorial presents a practical introduction to Answer Set Programming (ASP), aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while practically illustrating the overall solving process by examples.
Slides Resources Documentation Support
Answer Set Solving in Practice, Tutorial at AAAI'13
This halfday tutorial presents a practical introduction to Answer Set Programming (ASP), aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while practically illustrating the overall solving process by examples.
Slides Resources Documentation Support Video (Part I;Part II)
Answer Set Programming, Tutorial at FMCAD'12
This compact tutorial presents a practical introduction to Answer Set Programming (ASP), aiming at ASP's modeling methodology and systems.
Modeling and Solving in Answer Set Programming, Tutorial at KR'12
This halfday tutorial presents a practical introduction to Answer Set Programming (ASP), aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while practically illustrating the overall solving process by examples.
Slides Resources Documentation Support
Answer Set Solving in Practice, Tutorial at IJCAI'11
This fullday tutorial presents a practical introduction to Answer Set Programming (ASP), aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while practically illustrating the overall solving process by examples.
Slides Resources Documentation Support
Other Tutorials
 Esra Erdem, Joohyung Lee, and Yuliya Lierler. Theory and Practice of Answer Set Programming. Tutorial at AAAI'12. (Slides)
 Ilkka Niemelä. Answer Set Programming. Tutorial at ECAI'10. (Slides)
 Thomas Eiter, Giovambattista Ianni, Thomas Krennwallner. Answer Set Programming: A Primer. Tutorial at RW'09. (Notes)
 Tran Cao Son. Answer Set Programming. Tutorial 2005. (Slides)
 Vladimir Lifschitz. Answer Set Programming. Tutorial at ESSLLI'04. (Notes)
Selected Literature
 Michael Gelfond and Yulia Kahl. Knowledge Representation, Reasoning, and the Design of Intelligent Agents. Cambridge University Press, 2014.
 Vladimir Lifschitz. Foundations of logic programming. Principles of Knowledge Representation, 69127. CSLI Publications, 1996.
 Chitta Baral. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, 2003.
 Michael Gelfond. Answer sets. Handbook of Knowledge Representation, Chapter 7, 285316. Elsevier Science, 2008.
 Patrik Simons, Ilkka Niemelä, and Timo Soininen. Extending and implementing the stable model semantics. Artificial Intelligence, 138(12):181234, 2002.
 Vladimir Lifschitz. Answer set programming and plan generation. Artificial Intelligence, 138(12):3954, 2002.
 Nicola Leone, Gerald Pfeifer, Wolfgang Faber, Thomas Eiter, Georg Gottlob, Simona Perri, Francesco Scarcello. The DLV system for knowledge representation and reasoning. ACM Transactions on Computational Logic, 7(3):499562, 2006.
 Fangzhen Lin and Yuting Zhao. ASSAT: Computing answer sets of a logic program by SAT solvers. Artificial Intelligence, 157(12):115137, 2004.

Niklas Een and Niklas Sörensson.
An Extensible SATsolver.
Proceeedings SAT'03, 502518.
Springer, 2004.
 Potassco Literature
Potassco Slide Packages are licensed under a Creative Commons Attribution 3.0 Unported License.