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Partial Compilation of ASP Programs

Published online by Cambridge University Press:  20 September 2019

BERNARDO CUTERI Open the ORCID record for BERNARDO CUTERI [Opens in a new window] ,
CARMINE DODARO Open the ORCID record for CARMINE DODARO [Opens in a new window] ,
FRANCESCO RICCA Open the ORCID record for FRANCESCO RICCA [Opens in a new window]  and
PETER SCHÜLLER Open the ORCID record for PETER SCHÜLLER [Opens in a new window]
BERNARDO CUTERI
Affiliation:
DeMaCS, University of Calabria, Italy, (e-mails: [email protected], [email protected], [email protected])
CARMINE DODARO
Affiliation:
DeMaCS, University of Calabria, Italy, (e-mails: [email protected], [email protected], [email protected])
FRANCESCO RICCA
Affiliation:
DeMaCS, University of Calabria, Italy, (e-mails: [email protected], [email protected], [email protected])
PETER SCHÜLLER
Affiliation:
Knowledge-based Systems Group, TU Wien, Austria, (e-mail: [email protected])
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Abstract

Answer Set Programming (ASP) is a well-known declarative formalism in logic programming. Efficient implementations made it possible to apply ASP in many scenarios, ranging from deductive databases applications to the solution of hard combinatorial problems. State-of-the-art ASP systems are based on the traditional ground&solve approach and are general-purpose implementations, i.e., they are essentially built once for any kind of input program. In this paper, we propose an extended architecture for ASP systems, in which parts of the input program are compiled into an ad-hoc evaluation algorithm (i.e., we obtain a specific binary for a given program), and might not be subject to the grounding step. To this end, we identify a condition that allows the compilation of a sub-program, and present the related partial compilation technique. Importantly, we have implemented the new approach on top of a well-known ASP solver and conducted an experimental analysis on publicly-available benchmarks. Results show that our compilation-based approach improves on the state of the art in various scenarios, including cases in which the input program is stratified or the grounding blow-up makes the evaluation unpractical with traditional ASP systems.

Keywords

Answer set programmingGrounding bottleneckCompilation
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 857 - 873
Copyright
© Cambridge University Press 2019 

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References

Alviano, M., Dodaro, C., Leone, N., and Ricca, F. 2015. Advances in WASP. In LPNMR. LNCS, vol. 9345. Springer, 40–54.Google Scholar
Amendola, G., Greco, G., Leone, N., and Veltri, P. 2016. Modeling and reasoning about NTU games via answer set programming. In IJCAI. IJCAI/AAAI Press, 38–45.Google Scholar
Aschinger, M., Drescher, C., Friedrich, G., Gottlob, G., Jeavons, P., Ryabokon, A., and Thorstensen, E. 2011. Optimization methods for the partner units problem. In CPAIOR. 4–19.Google Scholar
Balduccini, M. and Lierler, Y. 2017. Constraint answer set solver EZCSP and why integration schemas matter. TPLP 17, 4, 462–515.Google Scholar
Bartholomew, M. and Lee, J. 2014. System aspmt2smt: Computing ASPMT theories by SMT solvers. In JELIA. Lecture Notes in Computer Science, vol. 8761. Springer, 529–542.Google Scholar
Bogaerts, B. and Weinzierl, A. 2018. Exploiting justifications for lazy grounding of answer set programs. In IJCAI. 1737–1745.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., Fuscà, D., Perri, S., and Zangari, J. 2017. I-DLV: the new intelligent grounder of DLV. Intelligenza Artificiale 11 , 1, 5–20.Google Scholar
Ceri, S., Gottlob, G., and Tanca, L. 1990. Logic Programming and Databases. Surveys in computer science. Springer.Google Scholar
Cuteri, B., Dodaro, C., Ricca, F., and Schüller, P. 2017. Constraints, lazy constraints, or propagators in ASP solving: An empirical analysis. TPLP 17, 5-6, 780–799.Google Scholar
Cuteri, B., Reale, K., and Ricca, F. 2019. A logic-based question answering system for cultural heritage. In JELIA. Lecture Notes in Computer Science, vol. 11468. Springer, 526–541.Google Scholar
Dal Palù, A., Dovier, A., Pontelli, E., and Rossi, G. 2009. GASP: answer set programming with lazy grounding. Fundam. Inform. 96 , 3, 297–322.Google Scholar
Dodaro, C., Gasteiger, P., Leone, N., Musitsch, B., Ricca, F., and Schekotihin, K. 2016. Combining answer set programming and domain heuristics for solving hard industrial problems (application paper). TPLP 16, 5-6, 653–669.Google Scholar
Dodaro, C. and Ricca, F. 2018. The external interface for extending WASP. TPLP in press CORR abs/1811.01692.Google Scholar
Eiter, T., Fink, M., Ianni, G., Krennwallner, T., Redl, C., and Schüller, P. 2016. A model building framework for answer set programming with external computations. TPLP 16, 4, 418–464.Google Scholar
Eiter, T., Ianni, G., and Krennwallner, T. 2009. Answer set programming: A primer. In Reasoning Web. Lecture Notes in Computer Science, vol. 5689. Springer, 40–110.Google Scholar
Erdem, E., Gelfond, M., and Leone, N. 2016. Applications of answer set programming. AI Magazine 37, 3, 53–68.Google Scholar
Erdem, E. and Öztok, U. 2015. Generating explanations for biomedical queries. TPLP 15, 1, 35–78.Google Scholar
Faber, W., Leone, N., and Perri, S. 2012. The intelligent grounder of DLV. In Correct Reasoning. Lecture Notes in Computer Science, vol. 7265. Springer, 247–264.Google Scholar
Garcia-Molina, H., Ullman, J. D., and Widom, J. 2009. Database systems - the complete book (2. ed.). Pearson Education.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., and Wanko, P. 2016. Theory solving made easy with clingo 5. In ICLP TCs. OASICS, vol. 52. 2:1–2:15.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Romero, J., and Schaub, T. 2015. Progress in clasp series 3. In LPNMR. Lecture Notes in Computer Science, vol. 9345. Springer, 368–383.Google Scholar
Gebser, M., Kaminski, R., König, A., and Schaub, T. 2011. Advances in gringo series 3. In LPNMR. LNCS, vol. 6645. Springer, 345–351.Google Scholar
Gebser, M., Leone, N., Maratea, M., Perri, S., Ricca, F., and Schaub, T. 2018. Evaluation techniques and systems for answer set programming: a survey. In IJCAI. ijcai.org, 5450–5456.Google Scholar
Gebser, M., Ryabokon, A., and Schenner, G. 2015. Combining heuristics for configuration problems using answer set programming. In LPNMR. Springer, 384–397.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Comput. 9, 3/4, 365–386.Google Scholar
Ianni, G., Martello, A., Panetta, C., and Terracina, G. 2009. Efficiently querying RDF(S) ontologies with answer set programming. J. Log. Comput. 19, 4, 671–695.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
Kojo, T., Männistö, T., and Soininen, T. 2003. Towards intelligent support for managing evolution of configurable software product families. In SCM. LNCS, vol. 2649. Springer, 86–101.Google Scholar
Koponen, L., Oikarinen, E., Janhunen, T., and Säilä, L. 2015. Optimizing phylogenetic supertrees using answer set programming. TPLP 15, 4-5, 604–619.Google Scholar
Lefevre, C., Béatrix, C., Stéphan, I., and Garcia, L. 2017. Asperix, a first-order forward chaining approach for answer set computing. Theory and Practice of Logic Programming 17, 3, 266–310.CrossRefGoogle Scholar
Leone, N., Allocca, C., Alviano, M., Calimeri, F., Civili, C., Costabile, R., Fiorentino, A., Fuscà, D., Germano, S., Laboccetta, G., Cuteri, B., Manna, M., Perri, S., Reale, K., Ricca, F., Veltri, P., and Zangari, J. 2019. Enhancing DLV for large-scale reasoning. In LPNMR. Lecture Notes in Computer Science, vol. 11481. Springer, 312–325.Google 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 TOCL 7, 3, 499–562.Google Scholar
Lierler, Y., Maratea, M., and Ricca, F. 2016. Systems, engineering environments, and competitions. AI Magazine 37, 3, 45–52.Google Scholar
Lifschitz, V. and Turner, H. 1994. Splitting a logic program. In ICLP. MIT Press, 23–37.Google Scholar
Manna, M., Ricca, F., and Terracina, G. 2015. Taming primary key violations to query large inconsistent data via ASP. TPLP 15, 4-5, 696–710.Google Scholar
Marileo, M. C. and Bertossi, L. E. 2010. The consistency extractor system: Answer set programs for consistent query answering in databases. Data Knowl. Eng. 69, 6, 545–572.Google Scholar
Muchnick, S. S. 1997. Advanced Compiler Design and Implementation. Morgan Kaufmann.Google Scholar
Nogueira, M., Balduccini, M., Gelfond, M., Watson, R., and Barry, M. 2001. An A Prolog decision support system for the space shuttle. In Answer Set Programming.Google Scholar
Ostrowski, M. and Schaub, T. 2012. ASP modulo CSP: the clingcon system. TPLP 12, 4-5, 485–503.Google Scholar
Simons, P., Niemelä, I., and Soininen, T. 2002. Extending and implementing the stable model semantics. Artif. Intell. 138, 1-2, 181–234.CrossRefGoogle Scholar
Ullman, J. D. 1988. Principles of Database and Knowledge-Base Systems, Volume I. Principles of computer science series, vol. 14. Computer Science Press.Google Scholar
Weinzierl, A. 2017. Blending Lazy-Grounding and CDNL Search for Answer-Set Solving. In LPNMR. LNCS, vol. 10377. 191–204.Google Scholar