Davide Fuscà
Francesco Calimeri
ABSTRACT
Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge.
Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts.
In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.
- C. Baral. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, 2003. Google Scholar
Digital Library
- G. Brewka, T. Eiter, and M. Truszczynski. Answer set programming at a glance. Commun. ACM, 54(12):92--103, 2011. Google ScholarDigital Library
- F. Buccafurri, N. Leone, and P. Rullo. Strong and Weak Constraints in Disjunctive Datalog. In J. Dix, U. Furbach, and A. Nerode, editors, Proceedings of the 4th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR'97), volume 1265 of Lecture Notes in AI (LNAI), pages 2--17, Dagstuhl, Germany, July 1997. Springer Verlag. Google ScholarDigital Library
- F. Calimeri, W. Faber, M. Gebser, G. Ianni, R. Kaminski, T. Krennwallner, N. Leone, F. Ricca, and T. Schaub. Asp-core-2: Input language format, 2012.Google Scholar
- F. Calimeri, D. Fuscà, S. Germano, S. Perri, and J. Zangari. Embedding ASP in mobile systems: discussion and preliminary implementations. In Proceedings of the Eighth Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP 2015), workshop of the 31st International Conference on Logic Programming (ICLP 2015), August 2015.Google Scholar
- F. Calimeri, D. Fuscà, S. Germano, S. Perri, and J. Zangari. embASP, since 2015. https://www.mat.unical.it/calimeri/projects/embasp/.Google Scholar
- F. Calimeri, M. Gebser, M. Maratea, and F. Ricca. Design and results of the fifth answer set programming competition. Artificial Intelligence, 231:151--181, 2016. Google ScholarDigital Library
- F. Calimeri and F. Ricca. On the application of the answer set programming system dlv in industry: a report from the field. Book Reviews, 2013(03), 2013.Google Scholar
- T. Eiter, W. Faber, N. Leone, and G. Pfeifer. Declarative problem-solving using the dlv system. In Logic-based artificial intelligence, pages 79--103. Springer, 2000. Google ScholarDigital Library
- T. Eiter, G. Ianni, and T. Krennwallner. Answer Set Programming: A Primer. In Reasoning Web. Semantic Technologies for Information Systems, 5th International Summer School - Tutorial Lectures, pages 40--110, Brixen-Bressanone, Italy, August-September 2009. Google ScholarDigital Library
- W. Faber, N. Leone, and G. Pfeifer. Recursive aggregates in disjunctive logic programs: Semantics and complexity. In J. J. Alferes and J. Leite, editors, Proceedings of the 9th European Conference on Artificial Intelligence (JELIA 2004), volume 3229 of Lecture Notes in AI (LNAI), pages 200--212. Springer Verlag, Sept. 2004.Google ScholarCross Ref
- O. Febbraro, G. Grasso, N. Leone, and F. Ricca. JASP: a framework for integrating Answer Set Programming with Java. In Proc. of KR2012. AAAI Press, 2012.Google Scholar
- E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design patterns: elements of, 1994.Google Scholar
- M. Gebser, R. Kaminski, B. Kaufmann, M. Ostrowski, T. Schaub, and M. Schneider. Potassco: The Potsdam answer set solving collection. AI Communications, 24(2):107--124, 2011. Google ScholarDigital Library
- M. Gebser, R. Kaminski, B. Kaufmann, and T. Schaub. Clingo = ASP + control: Preliminary report. In M. Leuschel and T. Schrijvers, editors, Technical Communications of the Thirtieth International Conference on Logic Programming (ICLP'14), volume arXiv:1405.3694v1, 2014. Theory and Practice of Logic Programming, Online Supplement.Google Scholar
- M. Gebser, M. Maratea, and F. Ricca. What's hot in the answer set programming competition. In D. Schuurmans and M. P. Wellman, editors, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., pages 4327--4329. AAAI Press, 2016.Google ScholarDigital Library
- M. Gelfond and N. Leone. Logic Programming and Knowledge Representation -- the A-Prolog perspective. Artificial Intelligence, 138(1-2):3--38, 2002. Google ScholarDigital Library
- M. Gelfond and V. Lifschitz. Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing, 9:365--385, 1991.Google ScholarDigital Library
- Google Activity Recognition API. https://developer.android.com/reference/com/google/android/gms/location/ActivityRecognition.html.Google Scholar
- N. Leone, G. Pfeifer, W. Faber, T. Eiter, G. Gottlob, S. Perri, and F. Scarcello. The DLV System for Knowledge Representation and Reasoning. ACM Transactions on Computational Logic, 7(3):499--562, July 2006. Google ScholarDigital Library
- N. Leone and F. Ricca. Answer set programming: A tour from the basics to advanced development tools and industrial applications. In RR2015, to appear, LNCS, 2015.Google ScholarCross Ref
- V. Lifschitz. Answer Set Planning. In D. D. Schreye, editor, Proceedings of the 16th International Conference on Logic Programming (ICLP'99), pages 23--37, Las Cruces, New Mexico, USA, Nov. 1999. The MIT Press. Google ScholarDigital Library
- V. W. Marek and M. Truszczyński. Stable Models and an Alternative Logic Programming Paradigm. In K. R. Apt, V. W. Marek, M. Truszczyński, and D. S. Warren, editors, The Logic Programming Paradigm--A 25-Year Perspective, pages 375--398. Springer Verlag, 1999.Google ScholarCross Ref
- I. Niemelä. Logic Programming with Stable Model Semantics as Constraint Programming Paradigm. Annals of Mathematics and Artificial Intelligence, 25(3-4):241--273, 1999. Google ScholarDigital Library
- Potassco, the Potsdam Answer Set Solving Collection. http://potassco.sourceforge.net/.Google Scholar
- T. C. Przymusinski. Stable Semantics for Disjunctive Programs. New Generation Computing, 9:401--424, 1991.Google ScholarDigital Library
- S. P. Radziszowski. Small Ramsey Numbers. The Electronic Journal of Combinatorics, 1, 1994. Revision 9: July 15, 2002.Google Scholar
- F. Ricca. The DLV Java Wrapper. In M. de Vos and A. Provetti, editors, Proceedings ASP03 - Answer Set Programming: Advances in Theory and Implementation, pages 305--316, Messina, Italy, Sept. 2003. Online at http://CEUR-WS.org/Vol-78/.Google Scholar
- M. Thimm. Tweety: A comprehensive collection of java libraries for logical aspects of artificial intelligence and knowledge representation. In KR, 2014.Google Scholar
- J. B. Warmer and A. G. Kleppe. The Object Constraint Language: Precise Modeling With Uml (Addison-Wesley Object Technology Series). Addison-Wesley Professional, Oct. 1998. Google ScholarDigital Library
Index Terms
- A framework for easing the development of applications embedding answer set programming
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