Hostname: page-component-76fb5796d-45l2p Total loading time: 0 Render date: 2024-04-26T03:38:27.562Z Has data issue: false hasContentIssue false
  • English
  • Français

First-order modular logic programs and their conservative extensions

Published online by Cambridge University Press:  14 October 2016

AMELIA HARRISON  and
YULIYA LIERLER
AMELIA HARRISON
Affiliation:
University of Texas at Austin (e-mail: ameliaj@cs.utexas.edu)
YULIYA LIERLER
Affiliation:
University of Nebraska Omaha (e-mail: ylierler@unomaha.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Modular logic programs provide a way of viewing logic programs as consisting of many independent, meaningful modules. This paper introduces first-order modular logic programs, which can capture the meaning of many answer set programs. We also introduce conservative extensions of such programs. This concept helps to identify strong relationships between modular programs as well as between traditional programs. We show how the notion of a conservative extension can be used to justify the common projection rewriting.

Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 16 , Special Issue 5-6: 32nd International Conference on Logic Programming , September 2016 , pp. 755 - 770
Copyright
Copyright © Cambridge University Press 2016 

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

Buddenhagen, M. and Lierler, Y. (2015). Performance tuning in answer set programming. In Logic Programming and Nonmonotonic Reasoning - 13th International Conference, LPNMR 2015, Lexington, KY, USA, September 27–30, 2015. Proceedings, 186–198.Google Scholar
Denecker, M., Lierler, Y., Truszczynski, M. and Vennekens, J. (2012). A Tarskian informal semantics for answer set programming. In Technical Communications of the 28th International Conference on Logic Programming (ICLP'12), LIPIcs, 17, 277–289.Google Scholar
Faber, W., Leone, N., Mateis, C. and Pfeifer, G. (1999). Using database optimization techniques for nonmonotonic reasoning, 135–139.Google Scholar
Ferraris, P. (2005). Answer sets for propositional theories. In Proceedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR), 119–131.CrossRefGoogle Scholar
Ferraris, P., Lee, J. and Lifschitz, V. (2011). Stable models and circumscription. Artificial Intelligence, 175, 236–263.Google Scholar
Ferraris, P., Lee, J., Lifschitz, V. and Palla, R. (2009). Symmetric splitting in the general theory of stable models. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 797–803.Google Scholar
Ferraris, P. and Lifschitz, V. (2005). Weight constraints as nested expressions. Theory and Practice of Logic Programming 5, 1–2, 45–74.Google Scholar
Lierler, Y. and Truszczyński, M. (2013). Modular answer set solving. In Proceedings of the 27th AAAI Conference on Artificial Intelligence.Google Scholar
Lifschitz, V. (2002). Answer set programming and plan generation. Artificial Intelligence 138, 39–54.Google Scholar
Lifschitz, V., Pearce, D. and Valverde, A. (2001). Strongly equivalent logic programs. ACM Transactions on Computational Logic 2, 526–541.Google Scholar
Lifschitz, V., Pearce, D. and Valverde, A. (2007). A characterization of strong equivalence for logic programs with variables. In Procedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR), 188–200.Google Scholar
Oikarinen, E. and Janhunen, T. (2008). Achieving compositionality of the stable model semantics for Smodels programs. Theory and Practice of Logic Programming 5–6, 717–761.Google Scholar
Wang, Y., Zhang, Y., Zhou, Y. and Zhang, M. (2014). Knowledge forgetting in answer set programming. Journal of Artificial Intelligence Research 50 1, 31–70.Google Scholar