2 Search Results for "Besnard, Philippe"


Document
Extended Abstract
A Decomposition Framework for Inconsistency Handling in Qualitative Spatial and Temporal Reasoning (Extended Abstract)

Authors: Yakoub Salhi and Michael Sioutis

Published in: LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)


Abstract
Dealing with inconsistency is a central problem in AI, due to the fact that inconsistency can arise for many reasons in real-world applications, such as context dependency, multi-source information, vagueness, noisy data, etc. Among the approaches that are involved in inconsistency handling, we can mention argumentation, non-monotonic reasoning, and paraconsistency, e.g., see [Philippe Besnard and Anthony Hunter, 2008; Gerhard Brewka et al., 1997; Koji Tanaka et al., 2013]. In the work of [Yakoub Salhi and Michael Sioutis, 2023], we are interested in dealing with inconsistency in the context of Qualitative Spatio-Temporal Reasoning (QSTR) [Ligozat, 2013]. QSTR is an AI framework that aims to mimic, natural, human-like representation and reasoning regarding space and time. This framework is applied to a variety of domains, such as qualitative case-based reasoning and learning [Thiago Pedro Donadon Homem et al., 2020] and visual sensemaking [Jakob Suchan et al., 2021]; the interested reader is referred to [Michael Sioutis and Diedrich Wolter, 2021] for a recent survey. Motivation. In [Yakoub Salhi and Michael Sioutis, 2023], we study the decomposition of an inconsistent constraint network into consistent subnetworks under, possible, mandatory constraints. To illustrate the interest of such a decomposition, we provide a simple example described in Figure 1. The QCN depicted in the top part of the figure corresponds to a description of an inconsistent plan. Further, we assume that the constraint Task A {before} Task B is mandatory. To handle inconsistency, this plan can be transformed into a decomposition of two consistent plans, depicted in the bottom part of the figure; this decomposition can be used, e.g., to capture the fact that Task C must be performed twice. More generally, network decomposition can be involved in inconsistency handling in several ways: it can be used to identify potential contexts that explain the presence of inconsistent information; it can also be used to restore consistency through a compromise between the components of a decomposition, e.g., by using belief merging [Jean-François Condotta et al., 2010]; in addition, QCN decomposition can be used as the basis for defining inconsistency measures. Contributions. We summarize the contributions of [Yakoub Salhi and Michael Sioutis, 2023] as follows. First, we propose a theoretical study of a problem that consists in decomposing an inconsistent QCN into a bounded number of consistent QCNs that may satisfy a specified part in the original QCN; intuitively, the required common part corresponds to the constraints that are considered necessary, if any. To this end, we provide upper bounds for the minimum number of components in a decomposition as well as computational complexity results. Secondly, we provide two methods for solving our decomposition problem. The first method corresponds to a greedy constraint-based algorithm, a variant of which involves the use of spanning trees; the basic idea of this variant is that any acyclic constraint graph in QSTR is consistent, and such a graph can be used as a starting point for building consistent components. The second method corresponds to a SAT-based encoding; every model of this encoding is used to construct a valid decomposition. Thirdly, we consider two optimization versions of the initial decomposition problem that focus on minimizing the number of components and maximizing the similarity between components, respectively. The similarity between two QCNs is quantified by the number of common non-universal constraints; the interest in maximizing the similarity lies mainly in the fact that it reduces the number of constraints that allow each component to be distinguished from the rest. Of course, our previous methods are adapted to tackle these optimization versions, too. Additionally, we introduce two inconsistency measures based on QCN decomposition, which can be seen as counterparts of measures for propositional KBs introduced in [Matthias Thimm, 2016; Meriem Ammoura et al., 2017], and show that they satisfy several desired properties in the literature. Finally, we provide implementations of our methods for computing decompositions and experimentally evaluate them using different metrics.

Cite as

Yakoub Salhi and Michael Sioutis. A Decomposition Framework for Inconsistency Handling in Qualitative Spatial and Temporal Reasoning (Extended Abstract). In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 16:1-16:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Copy BibTex To Clipboard

@InProceedings{salhi_et_al:LIPIcs.TIME.2023.16,
  author =	{Salhi, Yakoub and Sioutis, Michael},
  title =	{{A Decomposition Framework for Inconsistency Handling in Qualitative Spatial and Temporal Reasoning}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{16:1--16:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.16},
  URN =		{urn:nbn:de:0030-drops-191062},
  doi =		{10.4230/LIPIcs.TIME.2023.16},
  annote =	{Keywords: Spatial and Temporal Reasoning, Qualitative Constraints, Inconsistency Handling, Decomposition, Inconsistency Measures}
}
Document
Inconsistency Tolerance (Dagstuhl Seminar 03241)

Authors: Leopoldo Bertossi, Philippe Besnard, Anthony Hunter, and Torsten Schaub

Published in: Dagstuhl Seminar Reports. Dagstuhl Seminar Reports, Volume 1 (2021)


Abstract

Cite as

Leopoldo Bertossi, Philippe Besnard, Anthony Hunter, and Torsten Schaub. Inconsistency Tolerance (Dagstuhl Seminar 03241). Dagstuhl Seminar Report 382, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2003)


Copy BibTex To Clipboard

@TechReport{bertossi_et_al:DagSemRep.382,
  author =	{Bertossi, Leopoldo and Besnard, Philippe and Hunter, Anthony and Schaub, Torsten},
  title =	{{Inconsistency Tolerance (Dagstuhl Seminar 03241)}},
  pages =	{1--8},
  ISSN =	{1619-0203},
  year =	{2003},
  type = 	{Dagstuhl Seminar Report},
  number =	{382},
  institution =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemRep.382},
  URN =		{urn:nbn:de:0030-drops-152624},
  doi =		{10.4230/DagSemRep.382},
}
  • Refine by Author
  • 1 Bertossi, Leopoldo
  • 1 Besnard, Philippe
  • 1 Hunter, Anthony
  • 1 Salhi, Yakoub
  • 1 Schaub, Torsten
  • Show More...

  • Refine by Classification
  • 1 Computing methodologies → Spatial and physical reasoning
  • 1 Computing methodologies → Temporal reasoning
  • 1 Theory of computation → Constraint and logic programming

  • Refine by Keyword
  • 1 Decomposition
  • 1 Inconsistency Handling
  • 1 Inconsistency Measures
  • 1 Qualitative Constraints
  • 1 Spatial and Temporal Reasoning

  • Refine by Type
  • 2 document

  • Refine by Publication Year
  • 1 2003
  • 1 2023

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail