7 Search Results for "Gebser, Martin"

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
Position
DOI: 10.4230/TGDK.2.1.2
Grounding Stream Reasoning Research

Authors: Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

Abstract
In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic. In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream. This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
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Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer. Grounding Stream Reasoning Research. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 2:1-2:47, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{bonte_et_al:TGDK.2.1.2,
  author =	{Bonte, Pieter and Calbimonte, Jean-Paul and de Leng, Daniel and Dell'Aglio, Daniele and Della Valle, Emanuele and Eiter, Thomas and Giannini, Federico and Heintz, Fredrik and Schekotihin, Konstantin and Le-Phuoc, Danh and Mileo, Alessandra and Schneider, Patrik and Tommasini, Riccardo and Urbani, Jacopo and Ziffer, Giacomo},
  title =	{{Grounding Stream Reasoning Research}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:47},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.2},
  URN =		{urn:nbn:de:0030-drops-198597},
  doi =		{10.4230/TGDK.2.1.2},
  annote =	{Keywords: Stream Reasoning, Stream Processing, RDF streams, Streaming Linked Data, Continuous query processing, Temporal Logics, High-performance computing, Databases}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.36
Utilizing Constraint Optimization for Industrial Machine Workload Balancing

Authors: Benjamin Kovács, Pierre Tassel, Wolfgang Kohlenbrein, Philipp Schrott-Kostwein, and Martin Gebser

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
Efficient production scheduling is an important application area of constraint-based optimization techniques. Problem domains like flow- and job-shop scheduling have been extensive study targets, and solving approaches range from complete and local search to machine learning methods. In this paper, we devise and compare constraint-based optimization techniques for scheduling specialized manufacturing processes in the build-to-print business. The goal is to allocate production equipment such that customer orders are completed in time as good as possible, while respecting machine capacities and minimizing extra shifts required to resolve bottlenecks. To this end, we furnish several approaches for scheduling pending production tasks to one or more workdays for performing them. First, we propose a greedy custom algorithm that allows for quickly screening the effects of altering resource demands and availabilities. Moreover, we take advantage of such greedy solutions to parameterize and warm-start the optimization performed by integer linear programming (ILP) and constraint programming (CP) solvers on corresponding problem formulations. Our empirical evaluation is based on production data by Kostwein Holding GmbH, a worldwide supplier in the build-to-print business, and thus demonstrates the industrial applicability of our scheduling methods. We also present a user-friendly web interface for feeding the underlying solvers with customer order and equipment data, graphically displaying computed schedules, and facilitating the investigation of changed resource demands and availabilities, e.g., due to updating orders or including extra shifts.
Cite as

Benjamin Kovács, Pierre Tassel, Wolfgang Kohlenbrein, Philipp Schrott-Kostwein, and Martin Gebser. Utilizing Constraint Optimization for Industrial Machine Workload Balancing. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 36:1-36:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{kovacs_et_al:LIPIcs.CP.2021.36,
  author =	{Kov\'{a}cs, Benjamin and Tassel, Pierre and Kohlenbrein, Wolfgang and Schrott-Kostwein, Philipp and Gebser, Martin},
  title =	{{Utilizing Constraint Optimization for Industrial Machine Workload Balancing}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{36:1--36:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.36},
  URN =		{urn:nbn:de:0030-drops-153276},
  doi =		{10.4230/LIPIcs.CP.2021.36},
  annote =	{Keywords: application, production planning, production scheduling, linear programming, constraint programming, greedy algorithm, benchmarking}
}
Document
DOI: 10.4230/OASIcs.ICLP.2016.2
Theory Solving Made Easy with Clingo 5

Authors: Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Max Ostrowski, Torsten Schaub, and Philipp Wanko

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

Abstract
Answer Set Programming (ASP) is a model, ground, and solve paradigm. The integration of application- or theory-specific reasoning into ASP systems thus impacts on many if not all elements of its workflow, viz. input language, grounding, intermediate language, solving, and output format. We address this challenge with the fifth generation of the ASP system clingo and its grounding and solving components by equipping them with well-defined generic interfaces facilitating the manifold integration efforts. On the grounder's side, we introduce a generic way of specifying language extensions and propose an intermediate format accommodating their ground representation. At the solver end, this is accompanied by high-level interfaces easing the integration of theory propagators dealing with these extensions.
Cite as

Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Max Ostrowski, Torsten Schaub, and Philipp Wanko. Theory Solving Made Easy with Clingo 5. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{gebser_et_al:OASIcs.ICLP.2016.2,
  author =	{Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and Ostrowski, Max and Schaub, Torsten and Wanko, Philipp},
  title =	{{Theory Solving Made Easy with Clingo 5}},
  booktitle =	{Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)},
  pages =	{2:1--2:15},
  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.2016.2},
  URN =		{urn:nbn:de:0030-drops-67337},
  doi =		{10.4230/OASIcs.ICLP.2016.2},
  annote =	{Keywords: Answer Set Programming, Theory Language, Theory Propagation}
}
Document
DOI: 10.4230/OASIcs.ICLP.2016.5
Rewriting Optimization Statements in Answer-Set Programs

Authors: Jori Bomanson, Martin Gebser, and Tomi Janhunen

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

Abstract
Constraints on Pseudo-Boolean (PB) expressions can be translated into Conjunctive Normal Form (CNF) using several known translations. In Answer-Set Programming (ASP), analogous expressions appear in weight rules and optimization statements. Previously, we have translated weight rules into normal rules, using normalizations designed in accord with existing CNF encodings. In this work, we rededicate such designs to rewrite optimization statements in ASP. In this context, a rewrite of an optimization statement is a replacement accompanied by a set of normal rules that together replicate the original meaning. The goal is partially the same as in translating PB constraints or weight rules: to introduce new meaningful auxiliary atoms that may help a solver in the search for (optimal) solutions. In addition to adapting previous translations, we present selective rewriting techniques in order to meet the above goal while using only a limited amount of new rules and atoms. We experimentally evaluate these methods in preprocessing ASP optimization statements and then searching for optimal answer sets. The results exhibit significant advances in terms of numbers of optimally solved instances, reductions in search conflicts, and shortened computation times. By appropriate choices of rewriting techniques, improvements are made on instances involving both small and large weights. In particular, we show that selective rewriting is paramount on benchmarks involving large weights.
Cite as

Jori Bomanson, Martin Gebser, and Tomi Janhunen. Rewriting Optimization Statements in Answer-Set Programs. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{bomanson_et_al:OASIcs.ICLP.2016.5,
  author =	{Bomanson, Jori and Gebser, Martin and Janhunen, Tomi},
  title =	{{Rewriting Optimization Statements in Answer-Set Programs}},
  booktitle =	{Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)},
  pages =	{5:1--5:15},
  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.2016.5},
  URN =		{urn:nbn:de:0030-drops-67362},
  doi =		{10.4230/OASIcs.ICLP.2016.5},
  annote =	{Keywords: Answer-Set Programming, Pseudo-Boolean optimization, Translation methods}
}
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}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2011.1
Multi-Criteria Optimization in Answer Set Programming

Authors: Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub

Published in: LIPIcs, Volume 11, Technical Communications of the 27th International Conference on Logic Programming (ICLP'11) (2011)

Abstract
We elaborate upon new strategies and heuristics for solving multi-criteria optimization problems via Answer Set Programming (ASP). In particular, we conceive a new solving algorithm, based on conflictdriven learning, allowing for non-uniform descents during optimization. We apply these techniques to solve realistic Linux package configuration problems. To this end, we describe the Linux package configuration tool aspcud and compare its performance with systems pursuing alternative approaches.
Cite as

Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub. Multi-Criteria Optimization in Answer Set Programming. In Technical Communications of the 27th International Conference on Logic Programming (ICLP'11). Leibniz International Proceedings in Informatics (LIPIcs), Volume 11, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{gebser_et_al:LIPIcs.ICLP.2011.1,
  author =	{Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and Schaub, Torsten},
  title =	{{Multi-Criteria Optimization in Answer Set Programming}},
  booktitle =	{Technical Communications of the 27th International Conference on Logic Programming (ICLP'11)},
  pages =	{1--10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-31-6},
  ISSN =	{1868-8969},
  year =	{2011},
  volume =	{11},
  editor =	{Gallagher, John P. and Gelfond, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2011.1},
  URN =		{urn:nbn:de:0030-drops-31617},
  doi =		{10.4230/LIPIcs.ICLP.2011.1},
  annote =	{Keywords: Answer Set Programming, Multi-Criteria Optimization, Linux Package Configuration}
}
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