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Documents authored by Rettinger, Achim

Document
DOI: 10.4230/DagRep.11.8.11
Structure and Learning (Dagstuhl Seminar 21362)

Authors: Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, and Frank van Harmelen

Published in: Dagstuhl Reports, Volume 11, Issue 8 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 Year Roadmap for AI" at AAAI 2019. In this Dagstuhl seminar, we discussed related problems from an interdiscplinary perspective, in particular, Cognitive Science, Cognitive Psychology, Physics, Computational Humor, Linguistic, Machine Learning, and AI. This report overviews presentations and working groups during the seminar, and lists two open problems.
Cite as

Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, and Frank van Harmelen. Structure and Learning (Dagstuhl Seminar 21362). In Dagstuhl Reports, Volume 11, Issue 8, pp. 11-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{dong_et_al:DagRep.11.8.11,
  author =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  title =	{{Structure and Learning (Dagstuhl Seminar 21362)}},
  pages =	{11--34},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{8},
  editor =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.8.11},
  URN =		{urn:nbn:de:0030-drops-157670},
  doi =		{10.4230/DagRep.11.8.11},
  annote =	{Keywords: Knowledge graph, Machine learning, Neural-symbol unification}
}
Document
DOI: 10.4230/DagRep.5.5.43
Cross-Lingual Cross-Media Content Linking: Annotations and Joint Representations (Dagstuhl Seminar 15201)

Authors: Alexander G. Hauptmann, James Hodson, Juanzi Li, Nicu Sebe, and Achim Rettinger

Published in: Dagstuhl Reports, Volume 5, Issue 5 (2016)

Abstract
Dagstuhl Seminar 15201 was conducted on "Cross-Lingual Cross-Media Content Linking: Annotations and Joint Representations". Participants from around the world participated in the seminar and presented state-of-the-art and ongoing research related to the seminar topic. An executive summary of the seminar, abstracts of the talks from participants and working group discussions are presented in the forthcoming sections.
Cite as

Alexander G. Hauptmann, James Hodson, Juanzi Li, Nicu Sebe, and Achim Rettinger. Cross-Lingual Cross-Media Content Linking: Annotations and Joint Representations (Dagstuhl Seminar 15201). In Dagstuhl Reports, Volume 5, Issue 5, pp. 43-56, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@Article{hauptmann_et_al:DagRep.5.5.43,
  author =	{Hauptmann, Alexander G. and Hodson, James and Li, Juanzi and Sebe, Nicu and Rettinger, Achim},
  title =	{{Cross-Lingual Cross-Media Content Linking: Annotations and Joint Representations (Dagstuhl Seminar 15201)}},
  pages =	{43--56},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{5},
  number =	{5},
  editor =	{Hauptmann, Alexander G. and Hodson, James and Li, Juanzi and Sebe, Nicu and Rettinger, Achim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.5.5.43},
  URN =		{urn:nbn:de:0030-drops-53590},
  doi =		{10.4230/DagRep.5.5.43},
  annote =	{Keywords: Cross-lingual, Cross-media, Cross-modal, Natural language processing, Computer vision, Multimedia, Knowledge representation, Machine learning, Information extraction, Information retrieval}
}
Document
DOI: 10.4230/DagSemProc.09121.25
Partially Observable Markov Decision Processes with Behavioral Norms

Authors: Matthias Nickles and Achim Rettinger

Published in: Dagstuhl Seminar Proceedings, Volume 9121, Normative Multi-Agent Systems (2009)

Abstract
This extended abstract discusses various approaches to the constraining of Partially Observable Markov Decision Processes (POMDPs) using social norms and logical assertions in a dynamic logic framework. Whereas the exploitation of synergies among formal logic on the one hand and stochastic approaches and machine learning on the other is gaining significantly increasing interest since several years, most of the respective approaches fall into the category of relational learning in the widest sense, including inductive (stochastic) logic programming. In contrast, the use of formal knowledge (including knowledge about social norms) for the provision of hard constraints and prior knowledge for some stochastic learning or modeling task is much less frequently approached. Although we do not propose directly implementable technical solutions, it is hoped that this work is a useful contribution to a discussion about the usefulness and feasibility of approaches from norm research and formal logic in the context of stochastic behavioral models, and vice versa.
Cite as

Matthias Nickles and Achim Rettinger. Partially Observable Markov Decision Processes with Behavioral Norms. In Normative Multi-Agent Systems. Dagstuhl Seminar Proceedings, Volume 9121, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{nickles_et_al:DagSemProc.09121.25,
  author =	{Nickles, Matthias and Rettinger, Achim},
  title =	{{Partially Observable Markov Decision Processes with Behavioral Norms}},
  booktitle =	{Normative Multi-Agent Systems},
  pages =	{1--13},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9121},
  editor =	{Guido Boella and Pablo Noriega and Gabriella Pigozzi and Harko Verhagen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09121.25},
  URN =		{urn:nbn:de:0030-drops-19134},
  doi =		{10.4230/DagSemProc.09121.25},
  annote =	{Keywords: Norms, Partially Observable Markov Decision Processes, Deontic Logic, Propositional Dynamic Logic}
}
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