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Documents authored by De Raedt, Luc

Document
DOI: 10.4230/DagRep.13.10.182
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 23442)

Authors: Luc De Raedt, Ute Schmid, and Johannes Langer

Published in: Dagstuhl Reports, Volume 13, Issue 10 (2024)

Abstract
The Dagstuhl Seminar "Approaches and Applications of Inductive Programming" (AAIP) has taken place for the sixth time. The Dagstuhl Seminar series brings together researchers concerned with learning programs from input/output examples from different areas, mostly from machine learning and other branches of artificial intelligence research, cognitive scientists interested in human learning in complex domains, and researchers with a background in formal methods and programming languages. Main topics adressed in the AAIP 2023 seminar have been neurosymbolic approaches to IP bringing together learning and reasoning, IP as a post-hoc approach to explaining decision-making of deep learning blackbox models, and exploring the potential of deep learning approaches, especially large language models such as OpenAI Codex for IP. Topics discussed in working groups were Large Language Models and inductive programming in cognitive architectures, avoiding too much search in inductive programming, finding suitable benchmark problems, and evaluation criteria for interpretability and explainability of inductive programming.
Cite as

Luc De Raedt, Ute Schmid, and Johannes Langer. Approaches and Applications of Inductive Programming (Dagstuhl Seminar 23442). In Dagstuhl Reports, Volume 13, Issue 10, pp. 182-211, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{deraedt_et_al:DagRep.13.10.182,
  author =	{De Raedt, Luc and Schmid, Ute and Langer, Johannes},
  title =	{{Approaches and Applications of Inductive Programming (Dagstuhl Seminar 23442)}},
  pages =	{182--211},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{10},
  editor =	{De Raedt, Luc and Schmid, Ute and Langer, Johannes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.10.182},
  URN =		{urn:nbn:de:0030-drops-198397},
  doi =		{10.4230/DagRep.13.10.182},
  annote =	{Keywords: explainable ai, human-like machine learning, inductive logic programming, interpretable machine learning, neuro-symbolic ai}
}
Document
DOI: 10.4230/DagRep.11.4.20
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192)

Authors: Andrew Cropper, Luc De Raedt, Richard Evans, and Ute Schmid

Published in: Dagstuhl Reports, Volume 11, Issue 4 (2021)

Abstract
In this report the program and the outcomes of Dagstuhl Seminar 21192 "Approaches and Applications of Inductive Programming" is documented. The goal of inductive programming (IP) is to induce computer programs from data, typically input/output examples of a desired program. IP interests researchers from many areas of computer science, including machine learning, automated reasoning, program verification, and software engineering. Furthermore, IP contributes to research outside computer science, notably in cognitive science, where IP can help build models of human inductive learning and contribute methods for intelligent tutor systems. Building on the success of previous IP Dagstuhl seminars (13502, 15442, 17382, and 19202), the goal of this new edition of the seminar is to focus on IP methods which integrate learning and reasoning, scaling up IP methods to be applicable to more complex real world problems, and to further explore the potential of IP for explainable artificial intelligence (XAI), especially for interactive learning. The extended abstracts included in this report show recent advances in IP research. The included short report of the outcome of the discussion sessions additionally point out interesting interrelation between different aspects and possible new directions for IP.
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Andrew Cropper, Luc De Raedt, Richard Evans, and Ute Schmid. Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192). In Dagstuhl Reports, Volume 11, Issue 4, pp. 20-33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Article{cropper_et_al:DagRep.11.4.20,
  author =	{Cropper, Andrew and De Raedt, Luc and Evans, Richard and Schmid, Ute},
  title =	{{Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192)}},
  pages =	{20--33},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2021},
  volume =	{11},
  number =	{4},
  editor =	{Cropper, Andrew and De Raedt, Luc and Evans, Richard and Schmid, Ute},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.4.20},
  URN =		{urn:nbn:de:0030-drops-147975},
  doi =		{10.4230/DagRep.11.4.20},
  annote =	{Keywords: Interpretable Machine Learning, Explainable Artificial Intelligence, Interactive Learning, Human-like Computing, Inductive Logic Programming}
}
Document
DOI: 10.4230/DagRep.9.5.58
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202)

Authors: Luc De Raedt, Richard Evans, Stephen H. Muggleton, and Ute Schmid

Published in: Dagstuhl Reports, Volume 9, Issue 5 (2019)

Abstract
In this report the program and the outcomes of Dagstuhl Seminar 19202 "Approaches and Applications of Inductive Programming" is documented. After a short introduction to the state of the art to inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups is given.
Cite as

Luc De Raedt, Richard Evans, Stephen H. Muggleton, and Ute Schmid. Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). In Dagstuhl Reports, Volume 9, Issue 5, pp. 58-88, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{deraedt_et_al:DagRep.9.5.58,
  author =	{De Raedt, Luc and Evans, Richard and Muggleton, Stephen H. and Schmid, Ute},
  title =	{{Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202)}},
  pages =	{58--88},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{9},
  number =	{5},
  editor =	{De Raedt, Luc and Evans, Richard and Muggleton, Stephen H. and Schmid, Ute},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.5.58},
  URN =		{urn:nbn:de:0030-drops-113810},
  doi =		{10.4230/DagRep.9.5.58},
  annote =	{Keywords: Enduser programming, Explainable AI, Human-like computing, Inductive logic programming, Probabilistic programming}
}
Document
DOI: 10.4230/DagRep.8.9.154
Automating Data Science (Dagstuhl Seminar 18401)

Authors: Tijl De Bie, Luc De Raedt, Holger H. Hoos, and Padhraic Smyth

Published in: Dagstuhl Reports, Volume 8, Issue 9 (2019)

Abstract
Data science is concerned with the extraction of knowledge and insight, and ultimately societal or economic value, from data. It complements traditional statistics in that its object is data as it presents itself in the wild (often complex and heterogeneous, noisy, loosely structured, biased, etc.), rather than well-structured data sampled in carefully designed studies. It also has a strong computer science focus, and is related to popular areas such as big data, machine learning, data mining and knowledge discovery. Data science is becoming increasingly important with the abundance of big data, while the number of skilled data scientists is lagging. This has raised the question as to whether it is possible to automate data science in several contexts. First, from an artificial intelligence perspective, it is interesting to investigate whether (data) science (or portions of it) can be automated, as it is an activity currently requiring high levels of human expertise. Second, the field of machine learning has a long-standing interest in applying machine learning at the meta-level, in order to obtain better machine learning algorithms, yielding recent successes in automated parameter tuning, algorithm configuration and algorithm selection. Third, there is an interest in automating not only the model building process itself (cf. the Automated Statistician) but also in automating the preprocessing steps (data wrangling). This Dagstuhl seminar brought together researchers from all areas concerned with data science in order to study whether, to what extent, and how data science can be automated.
Cite as

Tijl De Bie, Luc De Raedt, Holger H. Hoos, and Padhraic Smyth. Automating Data Science (Dagstuhl Seminar 18401). In Dagstuhl Reports, Volume 8, Issue 9, pp. 154-181, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{debie_et_al:DagRep.8.9.154,
  author =	{De Bie, Tijl and De Raedt, Luc and Hoos, Holger H. and Smyth, Padhraic},
  title =	{{Automating Data Science  (Dagstuhl Seminar 18401)}},
  pages =	{154--181},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{9},
  editor =	{De Bie, Tijl and De Raedt, Luc and Hoos, Holger H. and Smyth, Padhraic},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.9.154},
  URN =		{urn:nbn:de:0030-drops-103443},
  doi =		{10.4230/DagRep.8.9.154},
  annote =	{Keywords: artificial intelligence, automated machine learning, automated scientific discovery, data science, inductive programming}
}
Document
DOI: 10.4230/DagRep.4.10.1
Constraints, Optimization and Data (Dagstuhl Seminar 14411)

Authors: Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, and Michele Sebag

Published in: Dagstuhl Reports, Volume 4, Issue 10 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 14411 "Constraints, Optimization and Data". Constraint programming and optimization have recently received considerable attention from the fields of machine learning and data mining; similarly, machine learning and data mining have received considerable attention from the fields of constraint programming and optimization. The goal of the seminar was to showcase recent progress in these different areas, with the objective of working towards a common basis of understanding, which should help to facilitate future synergies.
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Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, and Michele Sebag. Constraints, Optimization and Data (Dagstuhl Seminar 14411). In Dagstuhl Reports, Volume 4, Issue 10, pp. 1-31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@Article{deraedt_et_al:DagRep.4.10.1,
  author =	{De Raedt, Luc and Nijssen, Siegfried and O'Sullivan, Barry and Sebag, Michele},
  title =	{{Constraints, Optimization and Data (Dagstuhl Seminar 14411)}},
  pages =	{1--31},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{4},
  number =	{10},
  editor =	{De Raedt, Luc and Nijssen, Siegfried and O'Sullivan, Barry and Sebag, Michele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.4.10.1},
  URN =		{urn:nbn:de:0030-drops-48901},
  doi =		{10.4230/DagRep.4.10.1},
  annote =	{Keywords: Data mining, constraint programming, machine learning}
}
Document
DOI: 10.4230/DagRep.1.5.61
Constraint Programming meets Machine Learning and Data Mining (Dagstuhl Seminar 11201)

Authors: Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, and Pascal Van Hentenryck

Published in: Dagstuhl Reports, Volume 1, Issue 5 (2011)

Abstract
This report documents the programme and the outcomes of Dagstuhl Seminar 11201 "Constraint Programming meets Machine Learning and Data Mining". Our starting point in this seminar was that machine learning and data mining have developed largely independently from constraint programming till now, but that it is increasingly becoming clear that there are many opportunities for interactions between these areas: on the one hand, data mining and machine learning can be used to improve constraint solving; on the other hand, constraint solving can be used in data mining in machine learning. This seminar brought together prominent researchers from both communities to discuss these opportunities.
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Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, and Pascal Van Hentenryck. Constraint Programming meets Machine Learning and Data Mining (Dagstuhl Seminar 11201). In Dagstuhl Reports, Volume 1, Issue 5, pp. 61-83, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@Article{deraedt_et_al:DagRep.1.5.61,
  author =	{De Raedt, Luc and Nijssen, Siegfried and O'Sullivan, Barry and Van Hentenryck, Pascal},
  title =	{{Constraint Programming meets Machine Learning and Data Mining (Dagstuhl Seminar 11201)}},
  pages =	{61--83},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{5},
  editor =	{De Raedt, Luc and Nijssen, Siegfried and O'Sullivan, Barry and Van Hentenryck, Pascal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.1.5.61},
  URN =		{urn:nbn:de:0030-drops-32077},
  doi =		{10.4230/DagRep.1.5.61},
  annote =	{Keywords: Machine learning, data mining, constraint programming, constraints}
}
Document
DOI: 10.4230/DagSemProc.08041.1
08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications

Authors: Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass

Published in: Dagstuhl Seminar Proceedings, Volume 8041, Recurrent Neural Networks- Models, Capacities, and Applications (2008)

Abstract
From January 20 to 25 2008, the Dagstuhl Seminar 08041 ``Recurrent Neural Networks- Models, Capacities, and Applications'' 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. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass. 08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{deraedt_et_al:DagSemProc.08041.1,
  author =	{De Raedt, Luc and Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang},
  title =	{{08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08041.1},
  URN =		{urn:nbn:de:0030-drops-14250},
  doi =		{10.4230/DagSemProc.08041.1},
  annote =	{Keywords: Recurrent Neural Networks, Neural-Symbolic Integration, Biological Models, Hybrid Models, Relational Learning Echo State Networks, Spike Prediction, Unsupervised Recurrent Networks}
}
Document
DOI: 10.4230/DagSemProc.08041.2
08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications

Authors: Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass

Published in: Dagstuhl Seminar Proceedings, Volume 8041, Recurrent Neural Networks- Models, Capacities, and Applications (2008)

Abstract
The seminar centered around recurrent information processing in neural systems and its connections to brain sciences, on the one hand, and higher symbolic reasoning, on the other side. The goal was to explore connections across the disciplines and to tackle important questions which arise in all sub-disciplines such as representation of temporal information, generalization ability, inference, and learning.
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Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass. 08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{deraedt_et_al:DagSemProc.08041.2,
  author =	{De Raedt, Luc and Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang},
  title =	{{08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08041.2},
  URN =		{urn:nbn:de:0030-drops-14243},
  doi =		{10.4230/DagSemProc.08041.2},
  annote =	{Keywords: Recurrent networks}
}
Document
DOI: 10.4230/DagSemProc.07161.1
07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis

Authors: Luc De Raedt, Thomas Dietterich, Lise Getoor, Kristian Kersting, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 7161, Probabilistic, Logical and Relational Learning - A Further Synthesis (2008)

Abstract
From April 14 – 20, 2007, the Dagstuhl Seminar 07161 ``Probabilistic, Logical and Relational Learning - A Further Synthesis'' 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. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Luc De Raedt, Thomas Dietterich, Lise Getoor, Kristian Kersting, and Stephen H. Muggleton. 07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{deraedt_et_al:DagSemProc.07161.1,
  author =	{De Raedt, Luc and Dietterich, Thomas and Getoor, Lise and Kersting, Kristian and Muggleton, Stephen H.},
  title =	{{07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - A Further Synthesis},
  pages =	{1--21},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7161},
  editor =	{Luc de Raedt and Thomas Dietterich and Lise Getoor and Kristian Kersting and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07161.1},
  URN =		{urn:nbn:de:0030-drops-13885},
  doi =		{10.4230/DagSemProc.07161.1},
  annote =	{Keywords: Artificial Intelligence, Uncertainty in AI, Probabilistic Reasoning, Knowledge Representation, Logic Programming, Relational Learning, Inductive Logic Programming, Graphical Models, Statistical Relational Learning, First-Order Logical and Relational Probabilistic Languages}
}
Document
DOI: 10.4230/DagSemProc.05051.1
05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis

Authors: Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract
From 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis'' 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. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton. 05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{deraedt_et_al:DagSemProc.05051.1,
  author =	{De Raedt, Luc and Dietterich, Tom and Getoor, Lise and Muggleton, Stephen H.},
  title =	{{05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--27},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.1},
  URN =		{urn:nbn:de:0030-drops-4303},
  doi =		{10.4230/DagSemProc.05051.1},
  annote =	{Keywords: Statistical relational learning, probabilistic logic learning, inductive logic programming, knowledge representation, machine learning, uncertainty in artificial intelligence}
}
Document
DOI: 10.4230/DagSemProc.05051.2
05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis

Authors: Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract
A short report on the Dagstuhl seminar on Probabilistic, Logical and Relational Learning – Towards a Synthesis is given.
Cite as

Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton. 05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{deraedt_et_al:DagSemProc.05051.2,
  author =	{De Raedt, Luc and Dietterich, Tom and Getoor, Lise and Muggleton, Stephen H.},
  title =	{{05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.2},
  URN =		{urn:nbn:de:0030-drops-4121},
  doi =		{10.4230/DagSemProc.05051.2},
  annote =	{Keywords: Reasoning about Uncertainty, Relational and Logical Represenations, Statistical Relational Learning, Inductive Lgoic Programmign}
}
Document
DOI: 10.4230/DagSemProc.05051.8
Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting

Authors: Andrea Passerini, Paolo Frasconi, and Luc De Raedt

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract
An example-trace is a sequence of steps taken by a program on a given example input. Different approaches exist in order to exploit example-traces for learning, all explicitly inferring a target program from positive and negative traces. We generalize such idea by developing similarity measures betweeen traces in order to learn to discriminate between positive and negative ones. This allows to combine the expressiveness of inductive logic programming in representing knowledge to the statistical properties of kernel machines. Logic programs will be used to generate proofs of given visitor programs which exploit the available background knowledge, while kernel machines will be employed to learn from such proofs.
Cite as

Andrea Passerini, Paolo Frasconi, and Luc De Raedt. Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{passerini_et_al:DagSemProc.05051.8,
  author =	{Passerini, Andrea and Frasconi, Paolo and De Raedt, Luc},
  title =	{{Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--20},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.8},
  URN =		{urn:nbn:de:0030-drops-4171},
  doi =		{10.4230/DagSemProc.05051.8},
  annote =	{Keywords: Proof Trees, Logic Kernels, Learning from Traces}
}
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