Answer set programming (ASP) is a declarative programming paradigm oriented towards knowledge-intensive tasks and combinatorial search problems. Its main idea is to reduce the given search problem to computing stable models of a logic program, and to use an ASP solver to perform search. ASP is particularly suited for modeling incomplete, inconsistent and dynamic domains. It has become a major knowledge representation formalism and has been applied to several areas in AI including planning, diagnosis, information integration, and bioinformatics. Wide applications of ASP motivated various extensions to the language and implementations, including integrations of ASP with constraint satisfaction and description logics. Recently, the stable model semantics, a mathematical basis of ASP, is shown to be closely related to classical logic.

The tutorial will provide an interactive, educational session to introduce the current state of the art in declarative problem solving via ASP. The audience will walk away with an understanding of the mathematical foundation of ASP, algorithms and systems for computing answer sets, recent applications of ASP including biomedical query answering systems and cognitive robotics, and the applicability of ASP to problems of their own interests.

Prerequisite knowledge: Basic knowledge of first-order logic will be assumed.

Tutorial Slides

Contents

1. 1.General introduction and overview of applications of ASP
   

2. 2.Application of ASP in Biomedical Query Answering
   

3. 3.Introduction to ASP Language
   

4. 4.ASP programming methodology
   

5. 5.Application of ASP in Cognitive Robotics
   

6. 6.Answer Set Solving
   

7. 7.Relation of ASP to Classical Logic
   

Tutorial Speakers

Esra Erdem is a faculty member at Sabanci University. She received her Ph.D. in computer sciences at the University of Texas at Austin (2002), and visited University of Toronto and Vienna University of Technology for postdoctoral research (2002-2006). Her research is in the area of knowledge representation and reasoning.

Joohyung Lee is an assistant professor at Arizona State University. He received his Ph.D. in computer science from the University of Texas at Austin in 2005. His research interests are in knowledge representation, logic programming, computational logics, and security. His 2004 AAAI paper received an Honorable Mention for the Outstanding Paper Award.

Yuliya Lierler is a Computing Innovation Fellow Postdoc at the Computer Science Department at the University of Kentucky. She completed her Ph.D. in computer science at the University of Texas at Austin in 2010. She is interested in knowledge representation, automated reasoning, declarative problem solving, and natural language understanding.

Other Tutorials

1. •Martin Gebser, Torsten Schaub. Tutorial at IJCAI’11 (Slides)
   

2. •Ilkka Niemelä. Answer Set Programming. Tutorial at ECAI'10. (Slides)
   

3. •Thomas Eiter, Giovambattista Ianni, Thomas Krennwallner. Answer Set Programming: A Primer. Tutorial at RW'09. (Notes)
   

4. •Vladimir Lifschitz. Answer Set Programming. Tutorial at ESSLLI'04. (Notes)
   

Links

1. •Wikipedia article on Answer Set Programming
   

2. •2011 ASP Competition
   

3. •Potassco Teaching Material
   

4. •Texas Action Group
   

5. •Cognitive Robotics Group at Sabanci University
   

6. •Automated Reasoning Group at Arizona State University