Content: Publications

Preference Relations by Approximation

In Proc. the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR'18), pp. 2-11

Authors:Mario Alviano, Javier Romero, Torsten Schaub
Type:Article in Conference Proceedings
Publication Date:September 2018
Conference:the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR'18)

Abstract: Declarative languages for knowledge representation and reasoning provide constructs to define preference relations over the set of possible interpretations, so that preferred models represent optimal solutions of the encoded problem. We introduce the notion of approximation for replacing preference relations with stronger preference relations, that is, relations comparing more pairs of interpretations. Our aim is to accelerate the computation of a non-empty subset of the optimal solutions by means of highly specialized algorithms. We implement our approach in Answer Set Programming (ASP), where problems involving quantitative and qualitative preference relations can be addressed by ASPRIN, implementing a generic optimization algorithm. Unlike this, chains of approximations allow us to reduce several preference relations to the preference relations associated with ASP's native weak constraints and heuristic directives. In this way, ASPRIN can now take advantage of several highly optimized algorithms implemented by ASP solvers for computing optimal solutions.

BibTeX
@InProceedings{alrosc18a-2018,
  title =	{{Preference Relations by Approximation}},
  author =	{Mario Alviano and Javier Romero and Torsten Schaub},
  booktitle =	{Proc. of the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR'18)},
  year =	{2018},
  pages =	{2-11},
}