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A model building framework for answer set programming with external computations*

Published online by Cambridge University Press:  13 August 2015

THOMAS EITER ,
MICHAEL FINK ,
GIOVAMBATTISTA IANNI ,
THOMAS KRENNWALLNER ,
CHRISTOPH REDL  and
PETER SCHÜLLER
THOMAS EITER
Affiliation:
Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: eiter@kr.tuwien.ac.at, fink@kr.tuwien.ac.at)
MICHAEL FINK
Affiliation:
Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: eiter@kr.tuwien.ac.at, fink@kr.tuwien.ac.at)
GIOVAMBATTISTA IANNI
Affiliation:
Dipartimento di Matematica, Cubo 30B, Università della Calabria, 87036 Rende (CS), Italy (e-mail: ianni@mat.unical.it)
THOMAS KRENNWALLNER
Affiliation:
Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: tkren@kr.tuwien.ac.at, redl@kr.tuwien.ac.at)
CHRISTOPH REDL
Affiliation:
Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: tkren@kr.tuwien.ac.at, redl@kr.tuwien.ac.at)
PETER SCHÜLLER
Affiliation:
Computer Engineering Department, Faculty of Engineering, Marmara University, Goztepe Kampusu, Kadikoy 34722, Istanbul, Turkey (e-mail: peter.schuller@marmara.edu.tr)
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Abstract

As software systems are getting increasingly connected, there is a need for equipping nonmonotonic logic programs with access to external sources that are possibly remote and may contain information in heterogeneous formats. To cater for this need, hex programs were designed as a generalization of answer set programs with an API style interface that allows to access arbitrary external sources, providing great flexibility. Efficient evaluation of such programs however is challenging, and it requires to interleave external computation and model building; to decide when to switch between these tasks is difficult, and existing approaches have limited scalability in many real-world application scenarios. We present a new approach for the evaluation of logic programs with external source access, which is based on a configurable framework for dividing the non-ground program into possibly overlapping smaller parts called evaluation units. The latter will be processed by interleaving external evaluation and model building using an evaluation graph and a model graph, respectively, and by combining intermediate results. Experiments with our prototype implementation show a significant improvement compared to previous approaches. While designed for hex-programs, the new evaluation approach may be deployed to related rule-based formalisms as well.

Keywords

answer set programmingmodel buildingexternal computation, hex programs
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 16 , Issue 4 , July 2016 , pp. 418 - 464
Copyright
Copyright © Cambridge University Press 2015 

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Footnotes

*

This article is a significant extension of Eiter et al. (2011) and parts of Schüller (2012). This work has been supported by the Austrian Science Fund (FWF) Grants P24090 and P27730, and the Scientific and Technological Research Council of Turkey (TUBITAK) Grant 114E430.

References

Amir, E. and McIlraith, S. A. 2005. Partition-based logical reasoning for first-order and propositional theories. Artificial Intelligence 162, 1–2, 49–88.CrossRefGoogle Scholar
Bairakdar, S. E.-D., Dao-Tran, M., Eiter, T., Fink, M. and Krennwallner, T. 2010. The DMCS solver for distributed nonmonotonic multi-context systems. In European Conference on Logics in Artificial Intelligence (JELIA). Springer, 352–355.CrossRefGoogle Scholar
Balduccini, M. 2009. Representing constraint satisfaction problems in answer set programming. In Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP).Google Scholar
Balduccini, M. and Lierler, Y. 2013. Hybrid automated reasoning tools: From black-box to clear-box integration. In Answer Set Programming and Other Computing Paradigms (ASPOCP). 17–31.Google Scholar
Basol, S., Erdem, O., Fink, M. and Ianni, G. 2010. HEX programs with action atoms. In Technical Communications of the International Conference on Logic Programming (ICLP). 24–33.Google Scholar
Bögl, M., Eiter, T., Fink, M. and Schüller, P. 2010. The MCS-IE system for explaining inconsistency in multi-context systems. In European Conference on Logics in Artificial Intelligence (JELIA). 356–359.Google Scholar
Brewka, G. and Eiter, T. 2007. Equilibria in heterogeneous nonmonotonic multi-context systems. In AAAI Conference on Artificial Intelligence. AAAI Press, 385–390.Google Scholar
Brewka, G., Eiter, T. and Truszczynski, M. 2011. Answer set programming at a glance. Commun. ACM 54, 12, 92–103.Google Scholar
Calimeri, F., Cozza, S. and Ianni, G. 2007. External sources of knowledge and value invention in logic programming. Annals of Mathematics and Artificial Intelligence 50, 3–4, 333–361.Google Scholar
Calimeri, F., Cozza, S., Ianni, G. and Leone, N. 2008. Computable functions in ASP: Theory and implementation. In ICLP, Lecture Notes in Computer Science. Springer, 407–424.Google Scholar
Calimeri, F., Fink, M., Germano, S., Ianni, G., Redl, C. and Wimmer, A. 2013. AngryHEX: An artificial player for angry birds based on declarative knowledge bases. In National Workshop and Prize on Popularize Artificial Intelligence. 29–35.Google Scholar
Clarke, E., Grumberg, O., Jha, S., Lu, Y. and Veith, H. 2003. Counterexample-guided abstraction refinement for symbolic model checking. J. ACM 50, 5, 752–794.Google Scholar
Dao-Tran, M., Eiter, T. and Krennwallner, T. 2009. Realizing default logic over description logic knowledge bases. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Springer, 602–613.Google Scholar
Dung, P. M. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77, 2, 321–357.Google Scholar
Eiter, T., Fink, M., Ianni, G., Krennwallner, T. and Schüller, P. 2011. Pushing efficient evaluation of HEX programs by modular decomposition. In International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR). 93–106.CrossRefGoogle Scholar
Eiter, T., Fink, M. and Krennwallner, T. 2009. Decomposition of declarative knowledge bases with external functions. In International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press, 752–758.Google Scholar
Eiter, T., Fink, M., Krennwallner, T. and Redl, C. 2012. Conflict-driven ASP solving with external sources. Theory and Practice of Logic Programming 12, 4–5, 659–679.CrossRefGoogle Scholar
Eiter, T., Fink, M., Krennwallner, T. and Redl, C. 2013. Liberal safety criteria for HEX-programs. In 27th AAAI Conference (AAAI 2013), July 14–18, 2013, Bellevue, Washington, USA (July 14–18, 2013), desJardins, M. and Littman, M., Eds. AAAI Press.Google Scholar
Eiter, T., Fink, M., Krennwallner, T. and Redl, C. 2014a. Domain expansion for ASP-programs with external sources. Tech. Rep. INFSYS RR-1843-14-02, Institut für Informationssysteme, Technische Universität Wien, A-1040 Vienna, Austria.Google Scholar
Eiter, T., Fink, M., Krennwallner, T., Redl, C. and Schüller, P. 2014b. Efficient HEX-program evaluation based on unfounded sets. Journal of Artificial Intelligence Research 49, 269–321.Google Scholar
Eiter, T., Gottlob, G. and Mannila, H. 1997. Disjunctive datalog. ACM Transactions on Database Systems 22, 3, 364–418.Google Scholar
Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2008. Combining answer set programming with description logics for the semantic web. Artificial Intelligence 172, 12–13, 1495–1539.Google Scholar
Eiter, T., Ianni, G., Schindlauer, R. and Tompits, H. 2005. A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In International Joint Conference on Artificial Intelligence (IJCAI). Professional Book Center, 90–96.Google Scholar
Eiter, T., Ianni, G., Schindlauer, R. and Tompits, H. 2006. Effective integration of declarative rules with external evaluations for semantic-web reasoning. In European Semantic Web Conference (ESWC). Springer, 273–287.Google Scholar
Faber, W., Leone, N. and Pfeifer, G. 2004. Recursive aggregates in disjunctive logic programs: Semantics and complexity. In European Conference on Logics in Artificial Intelligence (JELIA). Springer, 200–212.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2014. Clingo = ASP + control: Preliminary report. Accessed 24 June 2015. URL: CoRR abs/1405.3694.Google Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2009a. Solution enumeration for projected boolean search problems. In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR). Springer, 71–86.Google Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2012. Conflict-driven answer set solving: From theory to practice. Artificial Intelligence 187–188, 52–89.Google Scholar
Gebser, M., Ostrowski, M. and Schaub, T. 2009b. Constraint answer set solving. In International Conference on Logic Programming (ICLP). Springer, 235–249.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Logic Programming: Proceedings of the 5th International Conference and Symposium, Kowalski, R. and Bowen, K., Eds. MIT Press, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 3/4, 365–386.Google Scholar
Havur, G., Ozbilgin, G., Erdem, E. and Patoglu, V. 2014. Geometric rearrangement of multiple movable objects on cluttered surfaces: A hybrid reasoning approach. In International Conference on Robotics and Automation (ICRA). 445–452.Google Scholar
Hoehndorf, R., Loebe, F., Kelso, J. and Herre, H. 2007. Representing default knowledge in biomedical ontologies: Application to the integration of anatomy and phenotype ontologies. BMC Bioinformatics 8, 1, 377.Google Scholar
Janhunen, T., Oikarinen, E., Tompits, H. and Woltran, S. 2009. Modularity aspects of disjunctive stable models. Journal of Artificial Intelligence Research 35, 813–857.Google Scholar
Järvisalo, M., Oikarinen, E., Janhunen, T. and Niemelä, I. 2009. A module-based framework for multi-language constraint modeling. In Logic Programming and Nonmonotonic Reasoning (LPNMR). 155–168.CrossRefGoogle Scholar
Lassila, O. and Swick, R. 1999. Resource description framework (RDF) model and syntax specification. Accessed 24 June 2015. URL: http://www.w3.org/TR/1999/REC-rdf-syntax-19990222.Google Scholar
Lierler, Y. 2014. Relating constraint answer set programming languages and algorithms. Artificial Intelligence 207, 1–22.Google Scholar
Lierler, Y. and Truszczynski, M. 2013. Modular answer set solving. In Late-Breaking Developments in the Field of Artificial Intelligence, Bellevue, Washington, USA, July 14–18, 2013. AAAI Workshops, vol. WS-13-17. AAAI.Google Scholar
Lifschitz, V. and Turner, H. 1994. Splitting a logic program. In Proceedings ICLP-94. MIT-Press, Santa Margherita Ligure, Italy, 23–38.Google Scholar
Linke, T. 2001. Graph theoretical characterization and computation of answer sets. In International Joint Conference on Artificial Intelligence (IJCAI). 641–645.Google Scholar
Linke, T. and Sarsakov, V. 2004. Suitable graphs for answer set programming. In LPAR, Baader, F. and Voronkov, A., Eds. Lecture Notes in Computer Science, vol. 3452. Springer, 154–168.Google Scholar
Mellarkod, V. S., Gelfond, M. and Zhang, Y. 2008. Integrating answer set programming and constraint logic programming. Annals of Mathematics and Artificial Intelligenc 53, 1–4, 251–287.CrossRefGoogle Scholar
Mosca, A. and Bernini, D. 2008. Ontology-driven geographic information system and dlvhex reasoning for material culture analysis. In Italian Workshop RiCeRcA at ICLP.Google Scholar
Niemelä, I. 1999. Logic programming with stable model semantics as constraint programming paradigm. Annals of Mathematics and Artificial Intelligenc 25, 3–4, 241–273.Google Scholar
Oikarinen, E. and Janhunen, T. 2008. Achieving compositionality of the stable model semantics for smodels programs. TPLP 8, 5–6, 717–761.Google Scholar
Ostrowski, M. and Schaub, T. 2012. ASP modulo CSP: The clingcon system. Theory and Practice of Logic Programming (TPLP) 12, 4–5, 485–503.Google Scholar
Palù, A. D., Dovier, A., Pontelli, E. and Rossi, G. 2009. Gasp: Answer set programming with lazy grounding. Fundamenta Informaticae 96, 3, 297–322.Google Scholar
Perri, S., Ricca, F. and Sirianni, M. 2010. A parallel ASP instantiator based on DLV. In Declarative Aspects of Multicore Programming (DAMP'10). LNCS. Springer, 73–82.Google Scholar
Polleres, A. 2007. From SPARQL to rules (and back). In International Conference on World Wide Web (WWW). ACM, 787–796.Google Scholar
Przymusinski, T. C. 1988. On the declarative semantics of deductive databases and logic programs. In Foundations of Deductive Databases and Logic Programming, Minker, J., Ed. Morgan Kaufman, 193–216.Google Scholar
Przymusinski, T. C. 1991. Stable semantics for disjunctive programs. New Generation Computing 9, 401–424.Google Scholar
Redl, C. 2014. Answer set programming with external sources: Algorithms and efficient evaluation. Ph.D. thesis, Vienna University of Technology.Google Scholar
Ross, K. 1994. Modular stratification and magic sets for datalog programs with negation. Journal of the ACM 41, 6, 1216–1267.Google Scholar
Schindlauer, R. 2006. Answer set programming for the semantic web. Ph.D. thesis, Vienna University of Technology, Vienna, Austria.Google Scholar
Schüller, P. 2012. Inconsistency in multi-context systems: Analysis and efficient evaluation. Ph.D. thesis, Vienna University of Technology, Vienna, Austria.Google Scholar
Schüller, P., Patoglu, V. and Erdem, E. 2013. A systematic analysis of levels of integration between low-level reasoning and task planning. In Workshop on Combining Task and Motion Planning at IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
Shen, Y., Wang, K., Eiter, T., Fink, M., Redl, C., Krennwallner, T. and Deng, J. 2014. FLP answer set semantics without circular justifications for general logic programs. Artificial Intelligence 213, 1–41.CrossRefGoogle Scholar
Tasharrofi, S. and Ternovska, E. 2011. A semantic account for modularity in multi-language modelling of search problems. In International Symposium on Frontiers of Combining Systems (FroCoS). 259–274.Google Scholar
Wang, Y., You, J., Yuan, L., Shen, Y. and Zhang, M. 2012. The loop formula based semantics of description logic programs. Theoretical Computer Science 415, 60–85.Google Scholar
Zakraoui, J. and Zagler, W. L. 2012. A method for generating CSS to improve web accessibility for old users. In Int. Conf. on Computers Helping People with Special Needs (ICCHP). 329–336.Google Scholar
Zirtiloglu, H. and Yolum, P. 2008. Ranking semantic information for e-government: complaints management. In International Workshop on Ontology-supported business intelligence (OBI). ACM.Google Scholar
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