8 Search Results for "Heintz, Fredrik"

Document
DOI: 10.4230/LIPIcs.CP.2025.5
Transition Dominance in Domain-Independent Dynamic Programming

Authors: J. Christopher Beck, Ryo Kuroiwa, Jimmy H. M. Lee, Peter J. Stuckey, and Allen Z. Zhong

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Domain-independent dynamic programming (DIDP) is a model-based paradigm for dynamic programming (DP) that enables users to define DP models based on a state transition system. Heuristic search-based solvers have demonstrated strong performance in solving combinatorial optimization problems. In this paper, we formally define transition dominance in DIDP, where one transition consistently leads to better solutions than another, allowing the search process to safely ignore dominated transitions. To facilitate the efficient use of transition dominance, we introduce an interface for defining transition dominance and propose the use of state functions to cache values, thereby avoiding redundant computations when verifying transition dominance. Experimental results on DP models across multiple problem classes indicate that incorporating transition dominance and state functions yields a 5 to 10 times speed-up on average for different search algorithms within the DIDP framework compared to the baseline.
Cite as

J. Christopher Beck, Ryo Kuroiwa, Jimmy H. M. Lee, Peter J. Stuckey, and Allen Z. Zhong. Transition Dominance in Domain-Independent Dynamic Programming. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 5:1-5:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{beck_et_al:LIPIcs.CP.2025.5,
  author =	{Beck, J. Christopher and Kuroiwa, Ryo and Lee, Jimmy H. M. and Stuckey, Peter J. and Zhong, Allen Z.},
  title =	{{Transition Dominance in Domain-Independent Dynamic Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{5:1--5:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.5},
  URN =		{urn:nbn:de:0030-drops-238661},
  doi =		{10.4230/LIPIcs.CP.2025.5},
  annote =	{Keywords: Dominance, Dynamic Programming, Combinatorial Optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.8
Breaking Symmetries with Involutions

Authors: Michael Codish and Mikoláš Janota

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Symmetry breaking for graphs and other combinatorial objects is notoriously hard. On the one hand, complete symmetry breaks are exponential in size. On the other hand, current, state-of-the-art, partial symmetry breaks are often considered too weak to be of practical use. Recently, the concept of graph patterns has been introduced and provides a concise representation for (large) sets of non-canonical graphs, i.e. graphs that are not lex-leaders and can be excluded from search. In particular, four (specific) graph patterns apply to identify about 3/4 of the set of all non-canonical graphs. Taking this approach further, we discover that graph patterns that derive from permutations that are involutions play an important role in the construction of symmetry breaks for graphs. We take advantage of this to guide the construction of partial and complete symmetry-breaking constraints based on graph patterns. The resulting constraints are small in size and strong in the number of symmetries they break.
Cite as

Michael Codish and Mikoláš Janota. Breaking Symmetries with Involutions. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{codish_et_al:LIPIcs.CP.2025.8,
  author =	{Codish, Michael and Janota, Mikol\'{a}\v{s}},
  title =	{{Breaking Symmetries with Involutions}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{8:1--8:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.8},
  URN =		{urn:nbn:de:0030-drops-238699},
  doi =		{10.4230/LIPIcs.CP.2025.8},
  annote =	{Keywords: graph symmetry, patterns, permutation, Ramsey graphs, greedy, CEGAR}
}
Document
DOI: 10.4230/LIPIcs.TIME.2024.17
Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN

Authors: Ibrahim Delibasoglu and Fredrik Heintz

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

Abstract
Anomaly detection in time series data is a critical task in various domains, including finance, healthcare, cybersecurity and industry. Traditional methods, such as time series decomposition, clustering, and density estimation, have provided robust solutions for identifying anomalies that exhibit distinct patterns or significant deviations from normal data distributions. Recent advancements in machine learning and deep learning have further enhanced these capabilities. This paper introduces a novel method for anomaly detection that combines the strengths of autoencoders and recurrent neural networks (RNNs) with an reconstruction error feedback mechanism based on Mean Squared Error. We compare our method against classical techniques and recent approaches like OmniAnomaly, which leverages stochastic recurrent neural networks, and the Anomaly Transformer, which introduces association discrepancy to capture long-range dependencies and DCDetector using contrastive representation learning with multi-scale dual attention. Experimental results demonstrate that our method achieves superior overall performance in terms of precision, recall, and F1 score. The source code is available at http://github.com/mribrahim/AE-FAR
Cite as

Ibrahim Delibasoglu and Fredrik Heintz. Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 17:1-17:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{delibasoglu_et_al:LIPIcs.TIME.2024.17,
  author =	{Delibasoglu, Ibrahim and Heintz, Fredrik},
  title =	{{Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{17:1--17:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.17},
  URN =		{urn:nbn:de:0030-drops-212244},
  doi =		{10.4230/LIPIcs.TIME.2024.17},
  annote =	{Keywords: Time series, Anomaly, Neural networks}
}
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.
Cite as

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
Survey
DOI: 10.4230/TGDK.1.1.11
How Does Knowledge Evolve in Open Knowledge Graphs?

Authors: Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs

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
Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.
Cite as

Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs. How Does Knowledge Evolve in Open Knowledge Graphs?. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 11:1-11:59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{polleres_et_al:TGDK.1.1.11,
  author =	{Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jim\'{e}nez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},
  title =	{{How Does Knowledge Evolve in Open Knowledge Graphs?}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{11:1--11:59},
  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.1.1.11},
  URN =		{urn:nbn:de:0030-drops-194855},
  doi =		{10.4230/TGDK.1.1.11},
  annote =	{Keywords: KG evolution, temporal KG, versioned KG, dynamic KG}
}
Document
DOI: 10.4230/DagRep.12.9.200
Cognitive Robotics (Dagstuhl Seminar 22391)

Authors: Fredrik Heintz, Gerhard Lakemeyer, and Sheila McIlraith

Published in: Dagstuhl Reports, Volume 12, Issue 9 (2023)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22391 on the topic of "Cognitive Robotics". Cognitive Robotics is concerned with endowing robots or software agents with higher level cognitive functions that involve reasoning, for example, about goals, perception, actions, the mental states of other agents, and collaborative task execution. The seminar is the latest event in a series of events on this topic that were initiated in 1998. With its roots in knowledge representation and reasoning, the program for this seminar was influenced by transformative advances in machine learning and deep learning, by recent advances in human-robot interactions, and by issues that arise in the development of trustworthy cognitive robotic systems. Reflective of this, the seminar featured sessions devoted to the following four themes: cognitive robotics and KR, verification of cognitive robots, human-robot interaction and robot ethics, and planning and learning. Each theme consisted of plenary talks, plenary discussions and working groups resulting in a research road map for the coming years. There was also a poster session where new or published results could be presented by the participants. The seminar was very successful and well received by the participants thanks to the excellent environment for exchanging ideas provided by Schloss Dagstuhl.
Cite as

Fredrik Heintz, Gerhard Lakemeyer, and Sheila McIlraith. Cognitive Robotics (Dagstuhl Seminar 22391). In Dagstuhl Reports, Volume 12, Issue 9, pp. 200-219, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{heintz_et_al:DagRep.12.9.200,
  author =	{Heintz, Fredrik and Lakemeyer, Gerhard and McIlraith, Sheila},
  title =	{{Cognitive Robotics (Dagstuhl Seminar 22391)}},
  pages =	{200--219},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{9},
  editor =	{Heintz, Fredrik and Lakemeyer, Gerhard and McIlraith, Sheila},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.200},
  URN =		{urn:nbn:de:0030-drops-178132},
  doi =		{10.4230/DagRep.12.9.200},
  annote =	{Keywords: Artificial Intelligence, Knowledge Representation and Reasoning, Cognitive Robotics, Verification, Human-robot Interaction, Robot Ethics, Machine Learning, Planning}
}
Document
DOI: 10.4230/DagSemProc.10081.13
Research with Collaborative Unmanned Aircraft Systems

Authors: Patrick Doherty, Jonas Kvarnström, Fredrik Heintz, D. Landen, and P.-M. Olsson

Published in: Dagstuhl Seminar Proceedings, Volume 10081, Cognitive Robotics (2010)

Abstract
We provide an overview of ongoing research which targets development of a principled framework for mixed-initiative interaction with unmanned aircraft systems (UAS). UASs are now becoming technologically mature enough to be integrated into civil society. Principled interaction between UASs and human resources is an essential component in their future uses in complex emergency services or bluelight scenarios. In our current research, we have targeted a triad of fundamental, interdependent conceptual issues: delegation, mixed- initiative interaction and adjustable autonomy, that is being used as a basis for developing a principled and well-defined framework for interaction. This can be used to clarify, validate and verify different types of interaction between human operators and UAS systems both theoretically and practically in UAS experimentation with our deployed platforms.
Cite as

Patrick Doherty, Jonas Kvarnström, Fredrik Heintz, D. Landen, and P.-M. Olsson. Research with Collaborative Unmanned Aircraft Systems. In Cognitive Robotics. Dagstuhl Seminar Proceedings, Volume 10081, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{doherty_et_al:DagSemProc.10081.13,
  author =	{Doherty, Patrick and Kvarnstr\"{o}m, Jonas and Heintz, Fredrik and Landen, D. and Olsson, P.-M.},
  title =	{{Research with Collaborative Unmanned Aircraft Systems}},
  booktitle =	{Cognitive Robotics},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10081},
  editor =	{Gerhard Lakemeyer and Hector J. Levesque and Fiora Pirri},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10081.13},
  URN =		{urn:nbn:de:0030-drops-26300},
  doi =		{10.4230/DagSemProc.10081.13},
  annote =	{Keywords: Multi-agent systems, robotics, human-robot interaction, delegation}
}
Document
DOI: 10.4230/DagSemProc.10081.16
Stream-Based Reasoning in DyKnow

Authors: Fredrik Heintz, Jonas Kvarnström, and Patrick Doherty

Published in: Dagstuhl Seminar Proceedings, Volume 10081, Cognitive Robotics (2010)

Abstract
The information available to modern autonomous systems is often in the form of streams. As the number of sensors and other stream sources increases there is a growing need for incremental reasoning about the incomplete content of sets of streams in order to draw relevant conclusions and react to new situations as quickly as possible. To act rationally, autonomous agents often depend on high level reasoning components that require crisp, symbolic knowledge about the environment. Extensive processing at many levels of abstraction is required to generate such knowledge from noisy, incomplete and quantitative sensor data. We define knowledge processing middleware as a systematic approach to integrating and organizing such processing, and argue that connecting processing components with streams provides essential support for steady and timely flows of information. DyKnow is a concrete and implemented instantiation of such middleware, providing support for stream reasoning at several levels. First, the formal kpl language allows the specification of streams connecting knowledge processes and the required properties of such streams. Second, chronicle recognition incrementally detects complex events from streams of more primitive events. Third, complex metric temporal formulas can be incrementally evaluated over streams of states. DyKnow and the stream reasoning techniques are described and motivated in the context of a UAV traffic monitoring application.
Cite as

Fredrik Heintz, Jonas Kvarnström, and Patrick Doherty. Stream-Based Reasoning in DyKnow. In Cognitive Robotics. Dagstuhl Seminar Proceedings, Volume 10081, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{heintz_et_al:DagSemProc.10081.16,
  author =	{Heintz, Fredrik and Kvarnstr\"{o}m, Jonas and Doherty, Patrick},
  title =	{{Stream-Based Reasoning in DyKnow}},
  booktitle =	{Cognitive Robotics},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10081},
  editor =	{Gerhard Lakemeyer and Hector J. Levesque and Fiora Pirri},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10081.16},
  URN =		{urn:nbn:de:0030-drops-26274},
  doi =		{10.4230/DagSemProc.10081.16},
  annote =	{Keywords: Knowledge representation, autonomous systems, stream-based reasoning}
}
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