TY - CHAP
T1 - Implementing ordered choice logic programming using answer set solvers
AU - De Vos, M
N1 - ID number: ISI:000189407700006
PY - 2004
Y1 - 2004
N2 - Ordered Choice Logic Programming (OCLP) allows for dynamic preference-based decision-making with multiple alternatives without the need for any form of negation. This complete absence of negation does not weaken the language as both forms (classical and as-failure) can be intuitively simulated in the language and eliminated using a simple pre-processor, making it also an easy language for users less familiar with logic programming. The semantics of the language is based on the preference between alternatives, yielding both a skeptical and a credulous approach. In this paper we demonstrate how OCLPs can be translated to semi-negative logic programs such that, depending on the transformation, the answer sets of the latter correspond with the skeptical/credulous answer sets of the former. By providing such a mapping, we have a mechanism for implementing OCLP using answer set solvers like Smodels or dlv. We end with a discussion of the complexity of our system and the reasoning tasks it can perform.
AB - Ordered Choice Logic Programming (OCLP) allows for dynamic preference-based decision-making with multiple alternatives without the need for any form of negation. This complete absence of negation does not weaken the language as both forms (classical and as-failure) can be intuitively simulated in the language and eliminated using a simple pre-processor, making it also an easy language for users less familiar with logic programming. The semantics of the language is based on the preference between alternatives, yielding both a skeptical and a credulous approach. In this paper we demonstrate how OCLPs can be translated to semi-negative logic programs such that, depending on the transformation, the answer sets of the latter correspond with the skeptical/credulous answer sets of the former. By providing such a mapping, we have a mechanism for implementing OCLP using answer set solvers like Smodels or dlv. We end with a discussion of the complexity of our system and the reasoning tasks it can perform.
M3 - Chapter or section
SN - 0302-9743
VL - 2942
T3 - Lecture Notes in Computer Science
SP - 59
EP - 77
BT - Foundations of Information and Knowledge Systems, Proceedings
ER -