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DOI: 10.4230/LIPIcs.STACS.2026.59

When Is Local Search Both Effective and Efficient?

Authors: Artem Kaznatcheev and Sofia Vazquez Alferez

Published in: LIPIcs, Volume 364, 43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026)

Abstract

Combinatorial optimization problems implicitly define fitness landscapes that combine the numeric structure of the "fitness" function to be maximized with the combinatorial structure of which assignments are "adjacent". Local search starts at an assignment in this landscape and successively moves assignments until no further improvement is possible among the adjacent assignments. Classic analyses of local search algorithms have focused more on the question of effectiveness ("did we find a good solution?") and often implicitly assumed that there are no doubts about their efficiency ("did we find it quickly?"). But there are many reasons to doubt the efficiency of local search. Even if we focus on fitness landscapes on the hypercube that are single peaked on every subcube (known as semismooth fitness landscapes, completely unimodal pseudo-Boolean functions, or acyclic unique sink orientations) where effectiveness is obvious, many local search algorithms are known to be inefficient. Since fitness landscapes are unwieldy exponentially large objects, we focus on their polynomial-sized representations by instances of valued constraint satisfaction problems (VCSP). We define a "direction" for valued constraints such that directed VCSPs generate semismooth fitness landscapes. We call directed VCSPs oriented if they do not have any pair of variables with arcs in both directions. Since recognizing if a VCSP-instance is directed or oriented is coNP-complete, we generalized oriented VCSPs as conditionally-smooth fitness landscapes where the structural property of "conditionally-smooth" is recognizable in polynomial time for a VCSP-instance. We prove that many popular local search algorithms like random ascent, simulated annealing, history-based rules, jumping rules, and the Kernighan-Lin heuristic are very efficient on conditionally-smooth landscapes. But conditionally-smooth landscapes are still expressive enough so that other well-regarded local search algorithms like steepest ascent and random facet require a super-polynomial number of steps to find the fitness peak.

Cite as

Artem Kaznatcheev and Sofia Vazquez Alferez. When Is Local Search Both Effective and Efficient?. In 43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 364, pp. 59:1-59:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kaznatcheev_et_al:LIPIcs.STACS.2026.59,
  author =	{Kaznatcheev, Artem and Vazquez Alferez, Sofia},
  title =	{{When Is Local Search Both Effective and Efficient?}},
  booktitle =	{43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026)},
  pages =	{59:1--59:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-412-3},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{364},
  editor =	{Mahajan, Meena and Manea, Florin and McIver, Annabelle and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2026.59},
  URN =		{urn:nbn:de:0030-drops-255480},
  doi =		{10.4230/LIPIcs.STACS.2026.59},
  annote =	{Keywords: valued constraint satisfaction problem, local search, algorithm analysis, constraint graphs, pseudo-Boolean functions, parameterized complexity}
}

@InProceedings{kaznatcheev_et_al:LIPIcs.STACS.2026.59,
  author =	{Kaznatcheev, Artem and Vazquez Alferez, Sofia},
  title =	{{When Is Local Search Both Effective and Efficient?}},
  booktitle =	{43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026)},
  pages =	{59:1--59:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-412-3},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{364},
  editor =	{Mahajan, Meena and Manea, Florin and McIver, Annabelle and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2026.59},
  URN =		{urn:nbn:de:0030-drops-255480},
  doi =		{10.4230/LIPIcs.STACS.2026.59},
  annote =	{Keywords: valued constraint satisfaction problem, local search, algorithm analysis, constraint graphs, pseudo-Boolean functions, parameterized complexity}
}

Document

DOI: 10.4230/LIPIcs.FSTTCS.2025.42

Degrees of Second and Higher-Order Polynomials

Authors: Donghyun Lim and Martin Ziegler

Published in: LIPIcs, Volume 360, 45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025)

Abstract

Second-order polynomials generalize classical (=first-order) ones in allowing for additional variables that range over functions rather than values. We are motivated by their applications in higher-order computational complexity theory, extending for instance discrete classes (like P/FP or PSPACE/FPSPACE) to operators in Analysis [http://doi.org/10.1137/S0097539794263452], [http://doi.org/10.1145/2189778.2189780]. The degree subclassifies ordinary polynomial growth into linear, quadratic, cubic, etc. To similarly classify second-order polynomials, we (well-)define their degree by structural induction as an "arctic" first-order polynomial: a term/expression over integer variable D and operations + and ⋅ and binary max(). This generalized degree turns out to transform nicely under (now two kinds of) polynomial composition. As examples, we collect and determine the degrees of previous and new asymptotic analyses of algorithms and operators receiving function/oracle arguments. Then we motivate and introduce third-order polynomials and their degrees as arctic second-order polynomials, along with their transformations under three kinds of composition. Proceeding to fourth order and beyond yields a hierarchy, with characterization in Simply Typed Lambda Calculus.

Cite as

Donghyun Lim and Martin Ziegler. Degrees of Second and Higher-Order Polynomials. In 45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 360, pp. 42:1-42:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{lim_et_al:LIPIcs.FSTTCS.2025.42,
  author =	{Lim, Donghyun and Ziegler, Martin},
  title =	{{Degrees of Second and Higher-Order Polynomials}},
  booktitle =	{45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025)},
  pages =	{42:1--42:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-406-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{360},
  editor =	{Aiswarya, C. and Mehta, Ruta and Roy, Subhajit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2025.42},
  URN =		{urn:nbn:de:0030-drops-251225},
  doi =		{10.4230/LIPIcs.FSTTCS.2025.42},
  annote =	{Keywords: Logic in Computer Science, Higher Order Program Analysis, Asymptotic Type Theory}
}

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Position

DOI: 10.4230/TGDK.1.1.2

Large Language Models and Knowledge Graphs: Opportunities and Challenges

Authors: Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract

Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.

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Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux. Large Language Models and Knowledge Graphs: Opportunities and Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 2:1-2:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{pan_et_al:TGDK.1.1.2,
  author =	{Pan, Jeff Z. and Razniewski, Simon and Kalo, Jan-Christoph and Singhania, Sneha and Chen, Jiaoyan and Dietze, Stefan and Jabeen, Hajira and Omeliyanenko, Janna and Zhang, Wen and Lissandrini, Matteo and Biswas, Russa and de Melo, Gerard and Bonifati, Angela and Vakaj, Edlira and Dragoni, Mauro and Graux, Damien},
  title =	{{Large Language Models and Knowledge Graphs: Opportunities and Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:38},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.2},
  URN =		{urn:nbn:de:0030-drops-194766},
  doi =		{10.4230/TGDK.1.1.2},
  annote =	{Keywords: Large Language Models, Pre-trained Language Models, Knowledge Graphs, Ontology, Retrieval Augmented Language Models}
}

Document

DOI: 10.4230/LITES-v005-i001-a004

A Static Analysis for the Minimization of Voters in Fault-Tolerant Circuits

Authors: Dmitry Burlyaev, Pascal Fradet, and Alain Girault

Published in: LITES, Volume 5, Issue 1 (2018). Leibniz Transactions on Embedded Systems, Volume 5, Issue 1

Abstract

We present a formal approach to minimize the number of voters in triple-modular redundant (TMR) sequential circuits. Our technique actually works on a single copy of the TMR circuit and considers a large class of fault mo dels of the form “at most 1 Single-Event Upset (SEU) or Single-Event Transient (SET) every k clock cycles”. Verification-based voter minimization guarantees that the resulting TMR circuit (i) is fault tolerant to the soft-errors defined by the fault model and (ii) is functionally equivalent to the initial TMR circuit. Our approach operates at the logic level and takes into account the input and output interface specifications of the circuit. Its implementation makes use of graph traversal algorithms, fixed-point iterations, and binary decision diagrams (BDD). Experimental results on the ITC’99 benchmark suite indicate that our method significantly decreases the number of inserted voters, yielding a hardware reduction of up to 55% and a clock frequency increase of up to 35% compared to full TMR. As our experiments show, if the SEU fault-model is replaced with the stricter fault-model of SET, it has a minor impact on the number of removed voters. On the other hand, BDD-based modelling of SET effects represents a more complex task than the modelling of an SEU as a bit-flip. We propose solutions for this task and explain the nature of encountered problems. We address scalability issues arising from formal verification with approximations and assess their efficiency and precision.

Cite as

Dmitry Burlyaev, Pascal Fradet, and Alain Girault. A Static Analysis for the Minimization of Voters in Fault-Tolerant Circuits. In LITES, Volume 5, Issue 1 (2018). Leibniz Transactions on Embedded Systems, Volume 5, Issue 1, pp. 04:1-04:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{burlyaev_et_al:LITES-v005-i001-a004,
  author =	{Burlyaev, Dmitry and Fradet, Pascal and Girault, Alain},
  title =	{{A Static Analysis for the Minimization of Voters in Fault-Tolerant Circuits}},
  journal =	{Leibniz Transactions on Embedded Systems},
  pages =	{04:1--04:26},
  ISSN =	{2199-2002},
  year =	{2018},
  volume =	{5},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES-v005-i001-a004},
  URN =		{urn:nbn:de:0030-drops-192753},
  doi =		{10.4230/LITES-v005-i001-a004},
  annote =	{Keywords: Digital Circuits, Fault-tolerance, Optimization, Static Analysis, Triple Modular Redundancy}
}

Document

DOI: 10.4230/DagRep.4.2.79

Robots Learning from Experiences (Dagstuhl Seminar 14081)

Authors: Anthony G. Cohn, Bernd Neumann, Alessandro Saffiotti, and Markus Vincze

Published in: Dagstuhl Reports, Volume 4, Issue 2 (2014)

Abstract

This report documents the programme and the outcomes of Dagstuhl Seminar 14081 "Robots Learning from Experiences". The report begins with a summary comprising information about the seminar topics, the programme, important discussion points, and conclusions. The main body of the report consists of the abstracts of 25 presentations given at the seminar, and of four reports about discussion groups.

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Anthony G. Cohn, Bernd Neumann, Alessandro Saffiotti, and Markus Vincze. Robots Learning from Experiences (Dagstuhl Seminar 14081). In Dagstuhl Reports, Volume 4, Issue 2, pp. 79-109, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@Article{cohn_et_al:DagRep.4.2.79,
  author =	{Cohn, Anthony G. and Neumann, Bernd and Saffiotti, Alessandro and Vincze, Markus},
  title =	{{Robots Learning from Experiences (Dagstuhl Seminar 14081)}},
  pages =	{79--109},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{4},
  number =	{2},
  editor =	{Cohn, Anthony G. and Neumann, Bernd and Saffiotti, Alessandro and Vincze, Markus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.4.2.79},
  URN =		{urn:nbn:de:0030-drops-45465},
  doi =		{10.4230/DagRep.4.2.79},
  annote =	{Keywords: Learning, experiences, cognitive systems}
}

Document

DOI: 10.4230/DagSemProc.08091.1

08091 Abstracts Collection – Logic and Probability for Scene Interpretation

Authors: Bernd Neumann, Anthony C. Cohn, David C. Hogg, and Ralf Möller

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

From 25.2.2008 to Friday 29.2.2008, the Dagstuhl Seminar 08091 ``Logic and Probability for Scene Interpretation'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper.

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Bernd Neumann, Anthony C. Cohn, David C. Hogg, and Ralf Möller. 08091 Abstracts Collection – Logic and Probability for Scene Interpretation. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{neumann_et_al:DagSemProc.08091.1,
  author =	{Neumann, Bernd and Cohn, Anthony C. and Hogg, David C. and M\"{o}ller, Ralf},
  title =	{{08091 Abstracts Collection – Logic and Probability for Scene Interpretation}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--17},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.1},
  URN =		{urn:nbn:de:0030-drops-16480},
  doi =		{10.4230/DagSemProc.08091.1},
  annote =	{Keywords: Logic, probabilities, scene interpretation}
}

Document

DOI: 10.4230/DagSemProc.08091.4

Architectural and Representational Requirements for Seeing Processes, Proto-affordances and Affordances

Authors: Aaron Sloman

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

This paper, combining the standpoints of philosophy and Artificial Intelligence with theoretical psychology, summarises several decades of investigation by the author of the variety of functions of vision in humans and other animals, pointing out that biological evolution has solved many more problems than are normally noticed. For example, the biological functions of human and animal vision are closely related to the ability of humans to do mathematics, including discovering and proving theorems in geometry, topology and arithmetic. Many of the phenomena discovered by psychologists and neuroscientists require sophisticated controlled laboratory settings and specialised measuring equipment, whereas the functions of vision reported here mostly require only careful attention to a wide range of everyday competences that easily go unnoticed. Currently available computer models and neural theories are very far from explaining those functions, so progress in explaining how vision works is more in need of new proposals for explanatory mechanisms than new laboratory data. Systematically formulating the requirements for such mechanisms is not easy. If we start by analysing familiar competences, that can suggest new experiments to clarify precise forms of these competences, how they develop within individuals, which other species have them, and how performance varies according to conditions. This will help to constrain requirements for models purporting to explain how the competences work. For example, Gibson’s theory of affordances needs a number of extensions, including allowing affordances to be composed in several ways from lower level proto-affordances. The paper ends with speculations regarding the need for new kinds of information-processing machinery to account for the phenomena.

Cite as

Aaron Sloman. Architectural and Representational Requirements for Seeing Processes, Proto-affordances and Affordances. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-57, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{sloman:DagSemProc.08091.4,
  author =	{Sloman, Aaron},
  title =	{{Architectural and Representational Requirements for Seeing Processes, Proto-affordances and Affordances}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--57},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.4},
  URN =		{urn:nbn:de:0030-drops-16569},
  doi =		{10.4230/DagSemProc.08091.4},
  annote =	{Keywords: Vision, affordances, architectures, development, design space}
}

Document

DOI: 10.4230/DagSemProc.08091.2

Abstraction, ontology and task-guidance for visual perception in robots

Authors: Matthias Schlemmer and Markus Vincze

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

For solving recognition tasks in order to navigate in unknown environments and to manipulate objects, humans seem to use at least the following crucial capabilities: abstraction (for storing higher-level concepts of things), common sense knowledge and prediction. Whereas the first and second provide the basis for situated recognition, the second and third serve for pruning the search space as it helps anticipating what (in an abstract sense) they will see next and where. The main goal of our current research is, how we could use such a kind of "common sense world knowledge" for guiding visual perception and understanding scenes. Therefore, we are combining an owl-ontology with the output of vision tools. The additional use of abstraction techniques tries to establish the possibility of detecting higher level concepts, such as arches composed of a variable number of parts. The goal is to finally find concepts such as doors and tables in arbitrary scenes in order to arrive at a generic recognition tool for home robots. The ontology should additionally provide task-specific information about the things to detect.

Cite as

Matthias Schlemmer and Markus Vincze. Abstraction, ontology and task-guidance for visual perception in robots. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{schlemmer_et_al:DagSemProc.08091.2,
  author =	{Schlemmer, Matthias and Vincze, Markus},
  title =	{{Abstraction, ontology and task-guidance for visual perception in robots}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.2},
  URN =		{urn:nbn:de:0030-drops-16081},
  doi =		{10.4230/DagSemProc.08091.2},
  annote =	{Keywords: Abstraction, ontology, task, vision}
}

Document

DOI: 10.4230/DagSemProc.08091.3

Approximate OWL Instance Retrieval with SCREECH

Authors: Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, and Tuvshintur Tserendorj

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

With the increasing interest in expressive ontologies for the Semantic Web, it is critical to develop scalable and efficient ontology reasoning techniques that can properly cope with very high data volumes. For certain application domains, approximate reasoning solutions, which trade soundness or completeness for increased reasoning speed, will help to deal with the high computational complexities which state of the art ontology reasoning tools have to face. In this paper, we present a comprehensive overview of the SCREECH approach to approximate instance retrieval with OWL ontologies, which is based on the KAON2 algorithms, facilitating a compilation of OWL DL TBoxes into Datalog, which is tractable in terms of data complexity. We present three different instantiations of the Screech approach, and report on experiments which show that the gain in efficiency outweighs the number of introduced mistakes in the reasoning process.

Cite as

Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, and Tuvshintur Tserendorj. Approximate OWL Instance Retrieval with SCREECH. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{hitzler_et_al:DagSemProc.08091.3,
  author =	{Hitzler, Pascal and Kr\"{o}tzsch, Markus and Rudolph, Sebastian and Tserendorj, Tuvshintur},
  title =	{{Approximate OWL Instance Retrieval with SCREECH}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.3},
  URN =		{urn:nbn:de:0030-drops-16157},
  doi =		{10.4230/DagSemProc.08091.3},
  annote =	{Keywords: Description logics, automated reasoning, approximate reasoning, Horn logic}
}

Document

DOI: 10.4230/DagSemProc.08091.5

Assimilating knowledge from neuroimages in schizophrenia diagnostics

Authors: Paulo Santos, Carlos Thomaz, Luiz Celiberto, Fabio Duran, Wagner Gattaz, and Geraldo Busatto

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

The aim of this article is to propose an integrated framework for classifying and describing patterns of disorders from medical images using a combination of image registration, linear discriminant analysis and region-based ontologies. In a first stage of this endeavour we are going to study and evaluate multivariate statistical methodologies to identify the most discriminating hyperplane separating two populations contained in the input data. This step has, as its major goal, the analysis of all the data simultaneously rather than feature by feature. The second stage of this work includes the development of an ontology whose aim is the assimilation and exploration of the knowledge contained in the results of the previous statistical methods. Automated knowledge discovery from images is the key motivation for the methods to be investigated in this research. We argue that such investigation provides a suitable framework for characterising the high complexity of MR images in schizophrenia.

Cite as

Paulo Santos, Carlos Thomaz, Luiz Celiberto, Fabio Duran, Wagner Gattaz, and Geraldo Busatto. Assimilating knowledge from neuroimages in schizophrenia diagnostics. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{santos_et_al:DagSemProc.08091.5,
  author =	{Santos, Paulo and Thomaz, Carlos and Celiberto, Luiz and Duran, Fabio and Gattaz, Wagner and Busatto, Geraldo},
  title =	{{Assimilating knowledge from neuroimages in schizophrenia diagnostics}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--25},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.5},
  URN =		{urn:nbn:de:0030-drops-16078},
  doi =		{10.4230/DagSemProc.08091.5},
  annote =	{Keywords: Statistical classification, spatial ontologies}
}

Document

DOI: 10.4230/DagSemProc.08091.6

Bayesian Compositional Hierarchies - A Probabilistic Structure for Scene Interpretation

Authors: Bernd Neumann

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

In high-level vision, it is often useful to organize conceptual models in compositional hierarchies. For example, models of building facades (which are used here as examples) can be described in terms of constituent parts such as balconies or window arrays which in turn may be further decomposed. While compositional hierarchies are widely used in scene interpretation, it is not clear how to model and exploit probabilistic dependencies which may exist within and between aggregates. In this contribution I present Bayesian Aggregate Hierarchies as a means to capture probabilistic dependencies in a compositional hierarchy. The formalism integrates well with object-centered representations and extends Bayesian Networks by allowing arbitrary probabilistic dependencies within aggregates. To obtain efficient inference procedures, the aggregate structure must possess abstraction properties which ensure that internal aggregate properties are only affected in accordance with the hierarchical structure. Using examples from the building domain, it is shown that probabilistic aggregate information can thus be integrated into a logic-based scene interpretation system and provide a preference measure for interpretation steps.

Cite as

Bernd Neumann. Bayesian Compositional Hierarchies - A Probabilistic Structure for Scene Interpretation. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{neumann:DagSemProc.08091.6,
  author =	{Neumann, Bernd},
  title =	{{Bayesian Compositional Hierarchies - A Probabilistic Structure for Scene Interpretation}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.6},
  URN =		{urn:nbn:de:0030-drops-16050},
  doi =		{10.4230/DagSemProc.08091.6},
  annote =	{Keywords: Scene interpretation, compositional hierarchy, probabilistic inference}
}

Document

DOI: 10.4230/DagSemProc.08091.7

Combining Logic and Probability in Tracking and Scene Interpretation

Authors: Brandon Bennett

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

The paper gives a high-level overview of some ways in which logical representations and reasoning can be used in computer vision applications, such as tracking and scene interpretation. The combination of logical and statistical approaches is also considered.

Cite as

Brandon Bennett. Combining Logic and Probability in Tracking and Scene Interpretation. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{bennett:DagSemProc.08091.7,
  author =	{Bennett, Brandon},
  title =	{{Combining Logic and Probability in  Tracking and Scene Interpretation}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--7},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.7},
  URN =		{urn:nbn:de:0030-drops-16120},
  doi =		{10.4230/DagSemProc.08091.7},
  annote =	{Keywords: Vision, Tracking, Logic, Probability, Spatio-Temporal Continuity}
}

Document

DOI: 10.4230/DagSemProc.08091.8

Implementing probabilistic description logics: An application to image interpretation

Authors: Ralf Möller and Tobias H. Näth

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

This paper presents an application of an optimized implementation of a probabilistic description logic defined by Giugno and Lukasiewicz [9] to the domain of image interpretation. This approach extends a description logic with so-called probabilistic constraints to allow for automated reasoning over formal ontologies in combination with probabilistic knowledge. We analyze the performance of current algorithms and investigate new optimization techniques.

Cite as

Ralf Möller and Tobias H. Näth. Implementing probabilistic description logics: An application to image interpretation. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{moller_et_al:DagSemProc.08091.8,
  author =	{M\"{o}ller, Ralf and N\"{a}th, Tobias H.},
  title =	{{Implementing probabilistic description logics: An application to image interpretation}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.8},
  URN =		{urn:nbn:de:0030-drops-16186},
  doi =		{10.4230/DagSemProc.08091.8},
  annote =	{Keywords: Probabilistic description logics, image interpretation probabilistic lexicographic entailment}
}

Document

DOI: 10.4230/DagSemProc.08091.9

Learning Grammatical Models for Object Recognition

Authors: Meg Aycinena Lippow, Leslie Pack Kaelbling, and Tomas Lozano-Perez

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

Many object recognition systems are limited by their inability to share common parts or structure among related object classes. This capability is desirable because it allows information about parts and relationships in one object class to be generalized to other classes for which it is relevant. This ability has the potential to allow effective parameter learning from fewer examples and better generalization of the learned models to unseen instances, and it enables more efficient recognition. With this goal in mind, we have designed a representation and recognition framework that captures structural variability and shared part structure within and among object classes. The framework uses probabilistic geometric grammars (PGGs) to represent object classes recursively in terms of their parts, thereby exploiting the hierarchical and substitutive structure inherent to many types of objects. To incorporate geometric and appearance information, we extend traditional probabilistic context-free grammars to represent distributions over the relative geometric characteristics of object parts as well as the appearance of primitive parts. We describe an efficient dynamic programming algorithm for object categorization and localization in images given a PGG model. We also develop an EM algorithm to estimate the parameters of a grammar structure from training data, and a search-based structure learning approach that finds a compact grammar to explain the image data while sharing substructure among classes. Finally, we describe a set of experiments that demonstrate empirically that the system provides a performance benefit.

Cite as

Meg Aycinena Lippow, Leslie Pack Kaelbling, and Tomas Lozano-Perez. Learning Grammatical Models for Object Recognition. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{aycinenalippow_et_al:DagSemProc.08091.9,
  author =	{Aycinena Lippow, Meg and Kaelbling, Leslie Pack and Lozano-Perez, Tomas},
  title =	{{Learning Grammatical Models for Object Recognition}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.9},
  URN =		{urn:nbn:de:0030-drops-16113},
  doi =		{10.4230/DagSemProc.08091.9},
  annote =	{Keywords: Object recognition, grammars, structure learning}
}

Document

DOI: 10.4230/DagSemProc.08091.10

Probabilistic Scene Modeling for Situated Computer Vision

Authors: Sven Wachsmuth and Agnes Swadzba

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract

Verbal statements and vision are a rich source of information in a human-machine interaction scenario. For this reason Situated Computer Vision aims to include knowledge about the communicative situation in which it takes place. This paper presents three approaches how to achieve scene models of such scenarios combining different modalities. Seeing (planar) scenes as configurations of parts leads to a probabilistic modeling with Bayes’ nets relating spoken utterances with results of an object recognition step. In the second approach parallel datasets form the basis for analyzing the statistical dependencies between them through learning a statistical translation model which maps between these datasets (here: words in a text and boundary fragments extracted in 2D images). The third approach deals with complex indoor scenes from which 3D data is acquired. Planar structures in the 3D points and statistics extracted on these planar patches describe the coarse spatial layouts of different indoor room types in such a way that a holistic classification scheme can be provided.

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Sven Wachsmuth and Agnes Swadzba. Probabilistic Scene Modeling for Situated Computer Vision. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{wachsmuth_et_al:DagSemProc.08091.10,
  author =	{Wachsmuth, Sven and Swadzba, Agnes},
  title =	{{Probabilistic Scene Modeling for Situated Computer Vision}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.10},
  URN =		{urn:nbn:de:0030-drops-16097},
  doi =		{10.4230/DagSemProc.08091.10},
  annote =	{Keywords: Scene Modeling, Human Robot Interaction}
}

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