3 Search Results for "Ruiz, Daniel"

Document
DOI: 10.4230/DagMan.10.1.1
Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)

Authors: James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter

Published in: Dagstuhl Manifestos, Volume 10, Issue 1 (2024)

Abstract
Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022,sser a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade.
Cite as

James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter. Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282). In Dagstuhl Manifestos, Volume 10, Issue 1, pp. 1-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{delgrande_et_al:DagMan.10.1.1,
  author =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  title =	{{Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)}},
  pages =	{1--61},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2024},
  volume =	{10},
  number =	{1},
  editor =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.10.1.1},
  URN =		{urn:nbn:de:0030-drops-201403},
  doi =		{10.4230/DagMan.10.1.1},
  annote =	{Keywords: Knowledge representation and reasoning, Applications of logics, Declarative representations, Formal logic}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2022.9
A Deterministic Memory Allocator for Dynamic Symbolic Execution

Authors: Daniel Schemmel, Julian Büning, Frank Busse, Martin Nowack, and Cristian Cadar

Published in: LIPIcs, Volume 222, 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
Dynamic symbolic execution (DSE) has established itself as an effective testing and analysis technique. While the memory model in DSE has attracted significant attention, the memory allocator has been largely ignored, despite its significant influence on DSE. In this paper, we discuss the different ways in which the memory allocator can influence DSE and the main design principles that a memory allocator for DSE needs to follow: support for external calls, cross-run and cross-path determinism, spatially and temporally distanced allocations, and stability. We then present KDAlloc, a deterministic allocator for DSE that is guided by these six design principles. We implement KDAlloc in KLEE, a popular DSE engine, and first show that it is competitive with KLEE’s default allocator in terms of performance and memory overhead, and in fact significantly improves performance in several cases. We then highlight its benefits for use-after-free error detection and two distinct DSE-based techniques: MoKlee, a system for saving DSE runs to disk and later (partially) restoring them, and SymLive, a system for finding infinite-loop bugs.
Cite as

Daniel Schemmel, Julian Büning, Frank Busse, Martin Nowack, and Cristian Cadar. A Deterministic Memory Allocator for Dynamic Symbolic Execution. In 36th European Conference on Object-Oriented Programming (ECOOP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 222, pp. 9:1-9:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{schemmel_et_al:LIPIcs.ECOOP.2022.9,
  author =	{Schemmel, Daniel and B\"{u}ning, Julian and Busse, Frank and Nowack, Martin and Cadar, Cristian},
  title =	{{A Deterministic Memory Allocator for Dynamic Symbolic Execution}},
  booktitle =	{36th European Conference on Object-Oriented Programming (ECOOP 2022)},
  pages =	{9:1--9:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-225-9},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{222},
  editor =	{Ali, Karim and Vitek, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2022.9},
  URN =		{urn:nbn:de:0030-drops-162372},
  doi =		{10.4230/LIPIcs.ECOOP.2022.9},
  annote =	{Keywords: memory allocation, dynamic symbolic execution}
}
Document
DOI: 10.4230/DagSemProc.07071.4
A Fast Algorithm for Matrix Balancing

Authors: Philip A. Knight and Daniel Ruiz

Published in: Dagstuhl Seminar Proceedings, Volume 7071, Web Information Retrieval and Linear Algebra Algorithms (2007)

Abstract
As long as a square nonnegative matrix A contains sufficient nonzero elements, then the matrix can be balanced, that is we can find a diagonal scaling of A that is doubly stochastic. A number of algorithms have been proposed to achieve the balancing, the most well known of these being the Sinkhorn-Knopp algorithm. In this paper we derive new algorithms based on inner-outer iteration schemes. We show that the Sinkhorn-Knopp algorithm belongs to this family, but other members can converge much more quickly. In particular, we show that while stationary iterative methods offer little or no improvement in many cases, a scheme using a preconditioned conjugate gradient method as the inner iteration can give quadratic convergence at low cost.
Cite as

Philip A. Knight and Daniel Ruiz. A Fast Algorithm for Matrix Balancing. In Web Information Retrieval and Linear Algebra Algorithms. Dagstuhl Seminar Proceedings, Volume 7071, pp. 1-18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{knight_et_al:DagSemProc.07071.4,
  author =	{Knight, Philip A. and Ruiz, Daniel},
  title =	{{A Fast Algorithm for Matrix Balancing}},
  booktitle =	{Web Information Retrieval and Linear Algebra Algorithms},
  pages =	{1--18},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7071},
  editor =	{Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07071.4},
  URN =		{urn:nbn:de:0030-drops-10735},
  doi =		{10.4230/DagSemProc.07071.4},
  annote =	{Keywords: Matrix balancing, Sinkhorn-Knopp algorithm, doubly stochastic matrix, conjugate gradient iteration}
}
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