Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-18T15:27:12.709Z Has data issue: false hasContentIssue false

On the stable model semantics for intensional functions

Published online by Cambridge University Press:  25 September 2013

MICHAEL BARTHOLOMEW
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering Arizona State University, Tempe, USA (e-mail: mjbartho@asu.edu, joolee@asu.edu)
JOOHYUNG LEE
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering Arizona State University, Tempe, USA (e-mail: mjbartho@asu.edu, joolee@asu.edu)

Abstract

Several extensions of the stable model semantics are available to describe ‘intensional’ functions—functions that can be described in terms of other functions and predicates by logic programs. Such functions are useful for expressing inertia and default behaviors of systems, and can be exploited for alleviating the grounding bottleneck involving functional fluents. However, the extensions were defined in different ways under different intuitions. In this paper we provide several reformulations of the extensions, and note that they are in fact closely related to each other and coincide on large syntactic classes of logic programs.

Type
Regular Papers
Copyright
Copyright © 2013 [MICHAEL BARTHOLOMEW and JOOHYUNG LEE] 

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

Balduccini, M. 2012a. An answer set solver for non-Herbrand programs: Progress report. In ICLP (Technical Communications), 49–60.Google Scholar
Balduccini, M. 2012b. A “conservative” approach to extending answer set programming with non-Herbrand functions. In Correct Reasoning - Essays on Logic-Based AI in Honour of Vladimir Lifschitz, 24–39.Google Scholar
Bartholomew, M. and Lee, J. 2012. Stable models of formulas with intensional functions. In Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR), 2–12.Google Scholar
Bartholomew, M. and Lee, J. 2013. Functional stable model semantics and answer set programming modulo theories. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), To appear.Google Scholar
Cabalar, P. 2011. Functional answer set programming. TPLP 11, 2–3, 203233.Google Scholar
Ferraris, P., Lee, J. and Lifschitz, V. 2011. Stable models and circumscription. Artificial Intelligence 175, 236263.10.1016/j.artint.2010.04.011CrossRefGoogle Scholar
Lifschitz, V. 2012. Logic programs with intensional functions. In Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR), 24–31.Google Scholar
Lifschitz, V., Morgenstern, L. and Plaisted, D. 2008. Knowledge representation and classical logic. In Handbook of Knowledge Representation, van Harmelen, F., Lifschitz, V. and Porter, B., Eds. Elsevier, 388.CrossRefGoogle Scholar
Truszczynski, M. 2012. Connecting first-order ASP and the logic FO(ID) through reducts. In Correct Reasoning - Essays on Logic-Based AI in Honour of Vladimir Lifschitz, 543–559.Google Scholar
Supplementary material: PDF

Bartholomew supplementary material

Bartholomew supplementary material

Download Bartholomew supplementary material(PDF)
PDF 338.9 KB