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Documents authored by Romero, Javier

Document
DOI: 10.4230/OASIcs.ICLP.2016.3
Computing Diverse Optimal Stable Models

Authors: Javier Romero, Torsten Schaub, and Philipp Wanko

Published in: OASIcs, Volume 52, Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)

Abstract
We introduce a comprehensive framework for computing diverse (or similar) solutions to logic programs with preferences. Our framework provides a wide spectrum of complete and incomplete methods for solving this task. Apart from proposing several new methods, it also accommodates existing ones and generalizes them to programs with preferences. Interestingly, this is accomplished by integrating and automating several basic ASP techniques - being of general interest even beyond diversification. The enabling factor of this lies in the recent advance of multi-shot ASP solving that provides us with fine-grained control over reasoning processes and abolishes the need for solver modifications and wrappers that were indispensable in previous approaches. Our framework is implemented as an extension to the ASP-based preference handling system asprin. We use the resulting system asprin 2 for an empirical evaluation of the diversification methods comprised in our framework.
Cite as

Javier Romero, Torsten Schaub, and Philipp Wanko. Computing Diverse Optimal Stable Models. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 3:1-3:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{romero_et_al:OASIcs.ICLP.2016.3,
  author =	{Romero, Javier and Schaub, Torsten and Wanko, Philipp},
  title =	{{Computing Diverse Optimal Stable Models}},
  booktitle =	{Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)},
  pages =	{3:1--3:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-007-1},
  ISSN =	{2190-6807},
  year =	{2016},
  volume =	{52},
  editor =	{Carro, Manuel and King, Andy and Saeedloei, Neda and De Vos, Marina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2016.3},
  URN =		{urn:nbn:de:0030-drops-67348},
  doi =		{10.4230/OASIcs.ICLP.2016.3},
  annote =	{Keywords: Answer Set Programming, Diversity, Similarity, Preferences}
}
Document
DOI: 10.4230/OASIcs.ICLP.2016.9
Answer Set Solving with Generalized Learned Constraints

Authors: Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Patrick Lühne, Javier Romero, and Torsten Schaub

Published in: OASIcs, Volume 52, Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)

Abstract
Conflict learning plays a key role in modern Boolean constraint solving. Advanced in satisfiability testing, it has meanwhile become a base technology in many neighboring fields, among them answer set programming (ASP). However, learned constraints are only valid for a currently solved problem instance and do not carry over to similar instances. We address this issue in ASP and introduce a framework featuring an integrated feedback loop that allows for reusing conflict constraints. The idea is to extract (propositional) conflict constraints, generalize and validate them, and reuse them as integrity constraints. Although we explore our approach in the context of dynamic applications based on transition systems, it is driven by the ultimate objective of overcoming the issue that learned knowledge is bound to specific problem instances. We implemented this workflow in two systems, namely, a variant of the ASP solver clasp that extracts integrity constraints along with a downstream system for generalizing and validating them.
Cite as

Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Patrick Lühne, Javier Romero, and Torsten Schaub. Answer Set Solving with Generalized Learned Constraints. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 9:1-9:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{gebser_et_al:OASIcs.ICLP.2016.9,
  author =	{Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and L\"{u}hne, Patrick and Romero, Javier and Schaub, Torsten},
  title =	{{Answer Set Solving with Generalized Learned Constraints}},
  booktitle =	{Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)},
  pages =	{9:1--9:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-007-1},
  ISSN =	{2190-6807},
  year =	{2016},
  volume =	{52},
  editor =	{Carro, Manuel and King, Andy and Saeedloei, Neda and De Vos, Marina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2016.9},
  URN =		{urn:nbn:de:0030-drops-67393},
  doi =		{10.4230/OASIcs.ICLP.2016.9},
  annote =	{Keywords: Answer Set Programming, Conflict Learning, Constraint Generalization, Generalized Constraint Feedback}
}
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