Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-06T09:04:39.074Z Has data issue: false hasContentIssue false
  • English
  • Français

Achievements in answer set programming*

Published online by Cambridge University Press:  30 August 2017

VLADIMIR LIFSCHITZ
VLADIMIR LIFSCHITZ*
Affiliation:
University of Texas, Austin, TX, USA (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes an approach to the methodology of answer set programming that can facilitate the design of encodings that are easy to understand and provably correct. Under this approach, after appending a rule or a small group of rules to the emerging program, we include a comment that states what has been “achieved” so far. This strategy allows us to set out our understanding of the design of the program by describing the roles of small parts of the program in a mathematically precise way.

Keywords

answer set programmingteaching logic programmingprogramming methodology
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 17 , Special Issue 5-6: 33rd International Conference on Logic Programming , September 2017 , pp. 961 - 973
Copyright
Copyright © Cambridge University Press 2017 

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.)

Footnotes

*

This research was partially supported by the National Science Foundation under Grant IIS-1422455

References

Brain, M., Erdem, E., Inoue, K., Oetsch, J., Pührer, J., Tompits, H. and Yilmaz, C. 2012. Event-sequence testing using answer-set programming. International Journal on Advances in Software 5, 237–251.Google Scholar
Brooks, D. R., Erdem, E., Erdoğan, S. T., Minett, J. W., and Ringe, D. 2007. Inferring phylogenetic trees using answer set programming. Journal of Automated Reasoning 39, 471–511.CrossRefGoogle Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F. and Schaub, T. 2012. ASP-Core-2: Input language format [online]. URL: https://www.mat.unical.it/aspcomp2013/files/ASP-CORE-2.03b.pdf [Accessed on August 15, 2017].Google Scholar
Charwat, G. and Pfandler, A. 2015. Democratix: A declarative approach to winner determination. In Proc. of the 4th International Conference on Algorithmic Decision Theory (ADT).CrossRefGoogle Scholar
Dijkstra, E. W. 1972. The humble programmer. Communications of the ACM 15, 859–866.CrossRefGoogle Scholar
Eiter, T., Leone, N., Mateis, C., Pfeifer, G. and Scarcello, F. 1998. The KR system dlv: Progress report, comparisons and benchmarks. In Proc. of International Conference on Principles of Knowledge Representation and Reasoning (KR), Cohn, A., Schubert, L., and Shapiro, S., Eds. 406–417.Google Scholar
Gebser, M., Harrison, A., Kaminski, R., Lifschitz, V. and Schaub, T. 2015. Abstract gringo. Theory and Practice of Logic Programming 15, 449–463.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Lindauer, M., Ostrowski, M., Romero, J., Schaub, T. and Thiele, S. 2015. Potassco User Guide, version 2.0 [online]. URL: https://sourceforge.net/projects/potassco/files/guide/2.0/guide-2.0.pdf/download.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2012. Answer Set Solving in Practice. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers.CrossRefGoogle Scholar
Gelfond, M. and Kahl, Y. 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1990. Logic programs with classical negation. In Proc. of International Conference on Logic Programming (ICLP), Warren, D. and Szeredi, P., Eds. 579–597.Google Scholar
Gelfond, M. and Lifschitz, V. 1993. Representing action and change by logic programs. Journal of Logic Programming 17, 301–322.CrossRefGoogle Scholar
Gelfond, M. and Przymusinska, H. 1996. Towards a theory of elaboration tolerance: Logic programming approach. International Journal of Software Engineering and Knowledge Engineering 6, 1, 89–112.CrossRefGoogle Scholar
Lifschitz, V. and Turner, H. 1999. Representing transition systems by logic programs. In Proc. of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR). 92–106.Google Scholar
Marek, V. and Truszczynski, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: A 25-Year Perspective. Springer Verlag, 375–398.CrossRefGoogle Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 241–273.Google Scholar