Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-20T05:04:54.820Z Has data issue: false hasContentIssue false
  • English
  • Français

Conflict-driven ASP solving with external sources

Published online by Cambridge University Press:  05 September 2012

THOMAS EITER ,
MICHAEL FINK ,
THOMAS KRENNWALLNER  and
CHRISTOPH REDL
THOMAS EITER
Affiliation:
Institut für Informationssysteme, Technische Universität WienFavoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: [email protected], [email protected], [email protected], [email protected])
MICHAEL FINK
Affiliation:
Institut für Informationssysteme, Technische Universität WienFavoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: [email protected], [email protected], [email protected], [email protected])
THOMAS KRENNWALLNER
Affiliation:
Institut für Informationssysteme, Technische Universität WienFavoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: [email protected], [email protected], [email protected], [email protected])
CHRISTOPH REDL
Affiliation:
Institut für Informationssysteme, Technische Universität WienFavoritenstraße 9-11, A-1040 Vienna, Austria (e-mail: [email protected], [email protected], [email protected], [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs and efficient solvers. To enable access to external information, hex-programs extend programs with external atoms, which allow for a bidirectional communication between the logic program and external sources of computation (e.g., description logic reasoners and Web resources). Current solvers evaluate hex-programs by a translation to ASP itself, in which values of external atoms are guessed and verified after the ordinary answer set computation. This elegant approach does not scale with the number of external accesses in general, in particular in presence of nondeterminism (which is instrumental for ASP). In this paper, we present a novel, native algorithm for evaluating hex-programs which uses learning techniques. In particular, we extend conflict-driven ASP solving techniques, which prevent the solver from running into the same conflict again, from ordinary to hex-programs. We show how to gain additional knowledge from external source evaluations and how to use it in a conflict-driven algorithm. We first target the uninformed case, i.e., when we have no extra information on external sources, and then extend our approach to the case where additional meta-information is available. Experiments show that learning from external sources can significantly decrease both the runtime and the number of considered candidate compatible sets.

Keywords

Answer set programmingnonmonotonic reasoningconflict-driven clause learning
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 12 , Issue 4-5: 28th International Conference on Logic Programming , July 2012 , pp. 659 - 679
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Brewka, G. and Eiter, T. 2007. Equilibria in heterogeneous nonmonotonic multi-context systems. In AAAI'07. AAAI Press, 385–390.Google Scholar
Brewka, G., Eiter, T. and Truszczyński, M. 2011. Answer set programming at a glance. Commun. ACM 54, 12, 92–103.Google Scholar
Biere, A., Heule, M. J. H., van Maaren, H. and Walsh, T., Eds. 2009. Handbook of Satisfiability. Frontiers in Artificial Intelligence and Applications, vol. 185. IOS Press.Google Scholar
Dao-Tran, M., Eiter, T. and Krennwallner, T. 2009. Realizing default logic over description logic knowledge bases. In ECSQARU'09. Springer, 602–613.Google Scholar
Drescher, C., Gebser, M., Grote, T., Kaufmann, B., König, A., Ostrowski, M. and Schaub, T. 2008. Conflict-driven disjunctive answer set solving. In KR'08. AAAI Press, 422–432.Google Scholar
Eiter, T., Fink, M., Ianni, G., Krennwallner, T. and Schüller, P. 2011. Pushing efficient evaluation of HEX programs by modular decomposition. In LPNMR'11. Springer, 93–106.Google Scholar
Eiter, T., Fink, M., Schüller, P. and Weinzierl, A. 2010. Finding explanations of inconsistency in multi-context systems. In KR'10. AAAI Press, 329–339.Google Scholar
Eiter, T., Ianni, G., Krennwallner, T. and Schindlauer, R. 2008. Exploiting conjunctive queries in description logic programs. Annals of Mathematics and Artificial Intelligence 53, 1–4, 115–152.CrossRefGoogle 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.CrossRefGoogle 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 IJCAI'05. 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 ESWC'06. Springer, 273–287.Google Scholar
Faber, W., Leone, N. and Pfeifer, G. 2011. Semantics and complexity of recursive aggregates in answer set programming. Artificial Intelligence 175, 1, 278–298.Google Scholar
Fages, F. 1994. Consistency of clark's completion and existence of stable models. J. Meth. Logic Comp. Sci. 1, 51–60.Google Scholar
Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T. and Schneider, M. T. 2011. Potassco: The Potsdam answer set solving collection. AI Communications 24, 2, 107–124.CrossRefGoogle 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. 2009. Constraint answer set solving. In ICLP'09. Springer, 235–249.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generat. Comput. 9, 3–4, 365–386.Google Scholar
Giunchiglia, E., Lierler, Y. and Maratea, M. 2006. Answer set programming based on propositional satisfiability. Journal of Automated Reasoning 36, 4, 345–377.Google Scholar
Goldberg, E. and Novikov, Y. 2007. BerkMin: A fast and robust SAT-solver. Discrete Applied Mathematics 155, 12, 1549–1561.CrossRefGoogle Scholar
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S. and Scarcello, F. 2006. The DLV system for knowledge representation and reasoning. ACM Transactions on Computer Logic 7, 3, 499–562.CrossRefGoogle Scholar
Lifschitz, V. 2002. Answer set programming and plan generation. Artificial Intelligence 138, 39–54.Google Scholar
Lin, F. and Zhao, Y. 2004. ASSAT: computing answer sets of a logic program by SAT solvers. Artificial Intelligence 157, 1–2, 115–137.CrossRefGoogle Scholar
Marek, V. W. and Truszczyński, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm. Springer, 375–398.CrossRefGoogle Scholar
Motik, B. and Sattler, U. 2006. A comparison of reasoning techniques for querying large description logic ABoxes. In LPAR'06. Springer, 227–241.Google Scholar
Niemelä, I. 1999. Logic Programming with Stable Model Semantics as Constraint Programming Paradigm. Annals of Mathematics and Artificial Intelligence 25, 3–4, 241–273.Google Scholar
Ostrowski, M. and Schaub, T. 2012. ASP modulo CSP: The clingcon system. Theor. Pract. Log. Prog., Special Issue 28th Intl. Conf. Logic Programming. To appear.CrossRefGoogle Scholar
Simons, P., Niemelä, I. and Soininen, T. 2002. Extending and implementing the stable model semantics. Artificial Intelligence 138, 1-2, 181–234.Google Scholar