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Complete Issue
Dagstuhl Reports, Volume 9, Issue 9, September 2019, Complete Issue

Abstract
Dagstuhl Reports, Volume 9, Issue 9, September 2019, Complete Issue

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Dagstuhl Reports, Volume 9, Issue 9, September 2019, Complete Issue. In Dagstuhl Reports, Volume 9, Issue 9, pp. 1-157, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@Article{DagRep.9.9,
  title =	{{Dagstuhl Reports, Volume 9, Issue 9, September 2019, Complete Issue}},
  pages =	{1--157},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.9},
  URN =		{urn:nbn:de:0030-drops-119895},
  doi =		{10.4230/DagRep.9.9},
  annote =	{Keywords: Dagstuhl Reports, Volume 9, Issue 9, September 2019, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 9, Issue 9, 2019

Abstract
Dagstuhl Reports, Table of Contents, Volume 9, Issue 9, 2019

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Dagstuhl Reports, Table of Contents, Volume 9, Issue 9, 2019. In Dagstuhl Reports, Volume 9, Issue 9, pp. i-ii, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@Article{DagRep.9.9.i,
  title =	{{Dagstuhl Reports, Table of Contents, Volume 9, Issue 9, 2019}},
  pages =	{i--ii},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.9.i},
  URN =		{urn:nbn:de:0030-drops-119902},
  doi =		{10.4230/DagRep.9.9.i},
  annote =	{Keywords: Table of Contents, Frontmatter}
}
Document
Logic and Learning (Dagstuhl Seminar 19361)

Authors: Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, and Daniel Neider


Abstract
The goal of building truly intelligent systems has forever been a central problem in computer science. While logic-based approaches of yore have had their successes and failures, the era of machine learning, specifically deep learning is also coming upon significant challenges. There is a growing consensus that the inductive reasoning and complex, high-dimensional pattern recognition capabilities of deep learning models need to be combined with symbolic (even programmatic), deductive capabilities traditionally developed in the logic and automated reasoning communities in order to achieve the next step towards building intelligent systems, including making progress at the frontier of hard problems such as explainable AI. However, these communities tend to be quite separate and interact only minimally, often at odds with each other upon the subject of the ``correct approach'' to AI. This report documents the efforts of Dagstuhl Seminar 19361 on ``Logic and Learning'' to bring these communities together in order to: (i) bridge the research efforts between them and foster an exchange of ideas in order to create unified formalisms and approaches that bear the advantages of both research methodologies; (ii) review and analyse the progress made across both communities; (iii) understand the subtleties and difficulties involved in solving hard problems using both perspectives; (iv) make attempts towards a consensus on what the hard problems are and what the elements of good solutions to these problems would be. The three focal points of the seminar were the strands of ``Logic for Machine Learning'', ``Machine Learning for Logic'', and ``Logic vs. Machine Learning''. The seminar format consisted of long and short talks, as well as breakout sessions. We summarise the motivations and proceedings of the seminar, and report on the abstracts of the talks and the results of the breakout sessions.

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Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, and Daniel Neider. Logic and Learning (Dagstuhl Seminar 19361). In Dagstuhl Reports, Volume 9, Issue 9, pp. 1-22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@Article{benedikt_et_al:DagRep.9.9.1,
  author =	{Benedikt, Michael and Kersting, Kristian and Kolaitis, Phokion G. and Neider, Daniel},
  title =	{{Logic and Learning (Dagstuhl Seminar 19361)}},
  pages =	{1--22},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{Benedikt, Michael and Kersting, Kristian and Kolaitis, Phokion G. and Neider, Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.9.1},
  URN =		{urn:nbn:de:0030-drops-118425},
  doi =		{10.4230/DagRep.9.9.1},
  annote =	{Keywords: Artificial Intelligence, Automated reasoning, Databases, Deep Learning, Inductive Logic Programming, Logic, Logic and Learning, Logic for Machine Learning, Logic vs. Machine Learning, Machine Learning, Machine Learning for Logic, Neurosymbolic methods}
}
Document
Deduction Beyond Satisfiability (Dagstuhl Seminar 19371)

Authors: Carsten Fuhs, Philipp Rümmer, Renate Schmidt, and Cesare Tinelli


Abstract
Research in automated deduction is traditionally focused on the problem of determining the satisfiability of formulas or, more generally, on solving logical problems with yes/no answers. Thanks to recent advances that have dramatically increased the power of automated deduction tools, there is now a growing interest in extending deduction techniques to attack logical problems with more complex answers. These include both problems with a long history, such as quantifier elimination, which are now being revisited in light of the new methods, as well as newer problems such as minimal unsatisfiable cores computation, model counting for propositional or first-order formulas, Boolean or SMT constraint optimization, generation of interpolants, abductive reasoning, and syntax-guided synthesis. Such problems arise in a variety of applications including the analysis of probabilistic systems (where properties like safety or liveness can be established only probabilistically), network verification (with relies on model counting), the computation of tight complexity bounds for programs, program synthesis, model checking (where interpolation or abductive reasoning can be used to achieve scale), and ontology-based information processing. The seminar brought together researchers and practitioners from many of the often disjoint sub-communities interested in the problems above. The unifying theme of the seminar was how to harness and extend the power of automated deduction methods to solve problems with more complex answers than binary ones.

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Carsten Fuhs, Philipp Rümmer, Renate Schmidt, and Cesare Tinelli. Deduction Beyond Satisfiability (Dagstuhl Seminar 19371). In Dagstuhl Reports, Volume 9, Issue 9, pp. 23-44, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@Article{fuhs_et_al:DagRep.9.9.23,
  author =	{Fuhs, Carsten and R\"{u}mmer, Philipp and Schmidt, Renate and Tinelli, Cesare},
  title =	{{Deduction Beyond Satisfiability (Dagstuhl Seminar 19371)}},
  pages =	{23--44},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{Fuhs, Carsten and R\"{u}mmer, Philipp and Schmidt, Renate and Tinelli, Cesare},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.9.23},
  URN =		{urn:nbn:de:0030-drops-118432},
  doi =		{10.4230/DagRep.9.9.23},
  annote =	{Keywords: abduction, automated deduction, interpolation, quantifier elimination, synthesis}
}
Document
Application-Oriented Computational Social Choice (Dagstuhl Seminar 19381)

Authors: Umberto Grandi, Stefan Napel, Rolf Niedermeier, and Kristen Brent Venable


Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 19381 ``Application-Oriented Computational Social Choice''. The seminar was organised around four focus topics: group recommender systems, fair allocation, electoral systems, and interactive democracy. For each topic, an invited survey was given by one of the participants. 26 participants presented their research in a regular talk, and two rump sessions allowed other participants to present their ongoing work and open problems in short talks. A special session was dedicated to software demonstrations, and 3 voting experiments were run during the seminar, also thanks to a mobile experimental laboratory that was brought to Dagstuhl. Finally, three afternoons were dedicated to group works.

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Umberto Grandi, Stefan Napel, Rolf Niedermeier, and Kristen Brent Venable. Application-Oriented Computational Social Choice (Dagstuhl Seminar 19381). In Dagstuhl Reports, Volume 9, Issue 9, pp. 45-65, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@Article{grandi_et_al:DagRep.9.9.45,
  author =	{Grandi, Umberto and Napel, Stefan and Niedermeier, Rolf and Venable, Kristen Brent},
  title =	{{Application-Oriented Computational Social Choice (Dagstuhl Seminar 19381)}},
  pages =	{45--65},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{Grandi, Umberto and Napel, Stefan and Niedermeier, Rolf and Venable, Kristen Brent},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.9.45},
  URN =		{urn:nbn:de:0030-drops-118445},
  doi =		{10.4230/DagRep.9.9.45},
  annote =	{Keywords: ai for the social good, collective decision making, multi-agent systems, social choice}
}
Document
Data Ecosystems: Sovereign Data Exchange among Organizations (Dagstuhl Seminar 19391)

Authors: Cinzia Cappiello, Avigdor Gal, Matthias Jarke, and Jakob Rehof


Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 19391 ``Data Ecosystems: Sovereign Data Exchange among Organizations''. The goal of the seminar was to bring together people from different disciplines (also outside the computer science area), in order to identify (i) a set of research challenges for the future development of data ecosystems and a catalogue of major approaches relevant to the field and (ii) a set of developed use cases of particular interest to the further development of data ecosystems. Towards the objectives, the seminar included tutorials, invited talks, presentations of open problems, working groups. This report presents the most relevant findings and contributions.

Cite as

Cinzia Cappiello, Avigdor Gal, Matthias Jarke, and Jakob Rehof. Data Ecosystems: Sovereign Data Exchange among Organizations (Dagstuhl Seminar 19391). In Dagstuhl Reports, Volume 9, Issue 9, pp. 66-134, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@Article{cappiello_et_al:DagRep.9.9.66,
  author =	{Cappiello, Cinzia and Gal, Avigdor and Jarke, Matthias and Rehof, Jakob},
  title =	{{Data Ecosystems: Sovereign Data Exchange among Organizations (Dagstuhl Seminar 19391)}},
  pages =	{66--134},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{Cappiello, Cinzia and Gal, Avigdor and Jarke, Matthias and Rehof, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.9.66},
  URN =		{urn:nbn:de:0030-drops-118450},
  doi =		{10.4230/DagRep.9.9.66},
  annote =	{Keywords: Data sovereignty, Data ecosystems, Business models, Data integration, Ethics}
}
Document
Comparative Theory for Graph Polynomials (Dagstuhl Seminar 19401)

Authors: Jo Ellis-Monaghan, Andrew Goodall, Iain Moffatt, and Kerri Morgan


Abstract
This report documents the programme and outcomes of Dagstuhl Seminar 19401 ``Comparative Theory for Graph Polynomials''. The study of graph polynomials has become increasingly active, with new applications and new graph polynomials being discovered each year. The genera of graph polynomials are diverse, and their interconnections are rich. Experts in the field are finding that proof techniques and results established in one area can be successfully extended to others. From this a general theory is emerging that encapsulates the deeper interconnections between families of graph polynomials and the various techniques, computational approaches, and methodologies applied to them. The overarching aim of this Seminar was to exploit commonalities among polynomial invariants of graphs, matroids, and related combinatorial structures. Model-theoretic, computational and other methods were used in order to initiate a comparative theory that collects the current state of knowledge into a more cohesive and powerful framework.

Cite as

Jo Ellis-Monaghan, Andrew Goodall, Iain Moffatt, and Kerri Morgan. Comparative Theory for Graph Polynomials (Dagstuhl Seminar 19401). In Dagstuhl Reports, Volume 9, Issue 9, pp. 135-155, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@Article{ellismonaghan_et_al:DagRep.9.9.135,
  author =	{Ellis-Monaghan, Jo and Goodall, Andrew and Moffatt, Iain and Morgan, Kerri},
  title =	{{Comparative Theory for Graph Polynomials (Dagstuhl Seminar 19401)}},
  pages =	{135--155},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{Ellis-Monaghan, Jo and Goodall, Andrew and Moffatt, Iain and Morgan, Kerri},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.9.135},
  URN =		{urn:nbn:de:0030-drops-118460},
  doi =		{10.4230/DagRep.9.9.135},
  annote =	{Keywords: graph polynomials, graph and matroid invariants, Tutte polynomial, topological and algebraic graph theory}
}

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