Result: Generality relations in answer set programming

Title:
Generality relations in answer set programming
Source:
Logic programming (22nd international conference, ICLP 2006)0ICLP 2006. :211-225
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 17 ref 1
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
Department of Computer and Communication Sciences, Wakayama University, Sakaedani, Wakayama 640-8510, Japan
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19104703
Database:
PASCAL Archive

Further Information

This paper studies generality relations on logic programs. Intuitively, a program P1 is more general than another program P2 if P1 gives us more information than P2. In this paper, we define various kinds of generality relations over nonmonotonic programs in the context of answer set programming. The semantic properties of generality relations are investigated based on domain theory, and both a minimal upper bound and a maximal lower bound are constructed for any pair of logic programs. We also introduce the concept of strong generality between logic programs and investigate its relationships to strong equivalence. These results provide a basic theory to compare the degree of incompleteness between nonmonotonic logic programs, and also have important applications to inductive logic programming and multi-agent systems.