Towards a theory of declarative knowledge
Foundations of deductive databases and logic programming
Foundations of disjunctive logic programming
Foundations of disjunctive logic programming
Complexity and expressive power of disjunctive logic programming (research overview)
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
ACM Transactions on Database Systems (TODS)
Foundations of logic programming
Principles of knowledge representation
Extending and implementing the stable model semantics
Artificial Intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Nested expressions in logic programs
Annals of Mathematics and Artificial Intelligence
Experimenting with heuristics for answer set programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The DLV Project: A Tour from Theory and Research to Applications and Market
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Hi-index | 0.00 |
Disjunctive Logic Programming (DLP) is an advanced formalism for Knowledge Representation and Reasoning (KRR). DLP is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class Σ P 2 (NP NP). Importantly, the DLP encodings are often simple and natural.In this paper, we single out some limitations of DLP for KRR, which cannot naturally express problems where the size of the disjunction is not known “a priori” (like N-Coloring), but it is part of the input. To overcome these limitations, we further enhance the knowledge modelling abilities of DLP, by extending this language by Parametric Connectives (OR and AND). These connectives allow us to represent compactly the disjunction/conjunction of a set of atoms having a given property. We formally define the semantics of the new language, named DLP ∨,∧ and we show the usefulness of the new constructs on relevant knowledge-based problems. We address implementation issues and discuss related works.