36 Search Results for "Ozaki, Ana"

Volume

OASIcs, Volume 99

International Research School in Artificial Intelligence in Bergen (AIB 2022)

AIB 2022, June 7-11, 2022, University of Bergen, Norway

Editors: Camille Bourgaux, Ana Ozaki, and Rafael Peñaloza

Volume

LIPIcs, Volume 178

27th International Symposium on Temporal Representation and Reasoning (TIME 2020)

TIME 2020, September 23-25, 2020, Bozen-Bolzano, Italy

Editors: Emilio Muñoz-Velasco, Ana Ozaki, and Martin Theobald

Artifact
Software
DOI: 10.4230/artifacts.22621
gruwesen/TOIROADS

Authors: Grunde Haraldsson Wesenberg

Abstract
Cite as

Grunde Haraldsson Wesenberg. gruwesen/TOIROADS (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@misc{dagpub-supp--paper-21723-urlgithub.com-gruwesen-TOIROADS,
   title = {{gruwesen/TOIROADS}}, 
   author = {Wesenberg, Grunde Haraldsson},
   note = {Software, version 1.0., Norwegian Research Council, project 322480, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:a86388944844fcc00f4cad67e1ec75a998f36eae;origin=https://github.com/gruwesen/TOIROADS;visit=swh:1:snp:3c7d6f6043fd8df8104755d878cdde71362099e4;anchor=swh:1:rev:dc099ff67dcf19839bee5d872d7ded3fea7da500}{\texttt{swh:1:dir:a86388944844fcc00f4cad67e1ec75a998f36eae}} (visited on 2024-12-18)},
   url = {https://github.com/gruwesen/TOIROADS},
   doi = {10.4230/artifacts.22621},
}
Document
Resource Paper
DOI: 10.4230/TGDK.2.2.6
TØIRoads: A Road Data Model Generation Tool

Authors: Grunde Haraldsson Wesenberg and Ana Ozaki

Published in: TGDK, Volume 2, Issue 2 (2024): Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 2, Issue 2

Abstract
We describe road data models which can represent high level features of a road network such as population, points of interest, and road length/cost and capacity, while abstracting from time and geographic location. Such abstraction allows for a simplified traffic usage and congestion analysis that focus on the high level features. We provide theoretical results regarding mass conservation and sufficient conditions for avoiding congestion within the model. We describe a road data model generation tool, which we call "TØI Roads". We also describe several parameters that can be specified by a TØI Roads user to create graph data that can serve as input for training graph neural networks (or another learning approach that receives graph data as input) for predicting congestion within the model. The road data model generation tool allows, for instance, the study of the effects of population growth and how changes in road capacity can mitigate traffic congestion.
Cite as

Grunde Haraldsson Wesenberg and Ana Ozaki. TØIRoads: A Road Data Model Generation Tool. In Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 2, pp. 6:1-6:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{wesenberg_et_al:TGDK.2.2.6,
  author =	{Wesenberg, Grunde Haraldsson and Ozaki, Ana},
  title =	{{T{\O}IRoads: A Road Data Model Generation Tool}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{6:1--6:12},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.2.6},
  URN =		{urn:nbn:de:0030-drops-225901},
  doi =		{10.4230/TGDK.2.2.6},
  annote =	{Keywords: Road Data, Transportation, Graph Neural Networks, Synthetic Dataset Generation}
}
Document
DOI: 10.4230/TGDK.2.3.2
Strong Faithfulness for ELH Ontology Embeddings

Authors: Victor Lacerda, Ana Ozaki, and Ricardo Guimarães

Published in: TGDK, Volume 2, Issue 3 (2024). Transactions on Graph Data and Knowledge, Volume 2, Issue 3

Abstract
Ontology embedding methods are powerful approaches to represent and reason over structured knowledge in various domains. One advantage of ontology embeddings over knowledge graph embeddings is their ability to capture and impose an underlying schema to which the model must conform. Despite advances, most current approaches do not guarantee that the resulting embedding respects the axioms the ontology entails. In this work, we formally prove that normalized ELH has the strong faithfulness property on convex geometric models, which means that there is an embedding that precisely captures the original ontology. We present a region-based geometric model for embedding normalized ELH ontologies into a continuous vector space. To prove strong faithfulness, our construction takes advantage of the fact that normalized ELH has a finite canonical model. We first prove the statement assuming (possibly) non-convex regions, allowing us to keep the required dimensions low. Then, we impose convexity on the regions and show the property still holds. Finally, we consider reasoning tasks on geometric models and analyze the complexity in the class of convex geometric models used for proving strong faithfulness.
Cite as

Victor Lacerda, Ana Ozaki, and Ricardo Guimarães. Strong Faithfulness for ELH Ontology Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 3, pp. 2:1-2:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{lacerda_et_al:TGDK.2.3.2,
  author =	{Lacerda, Victor and Ozaki, Ana and Guimar\~{a}es, Ricardo},
  title =	{{Strong Faithfulness for ELH Ontology Embeddings}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:29},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.3.2},
  URN =		{urn:nbn:de:0030-drops-225965},
  doi =		{10.4230/TGDK.2.3.2},
  annote =	{Keywords: Knowledge Graph Embeddings, Ontologies, Description Logic}
}
Document
DOI: 10.4230/DagMan.10.1.1
Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)

Authors: James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter

Published in: Dagstuhl Manifestos, Volume 10, Issue 1 (2024)

Abstract
Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022,sser a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade.
Cite as

James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter. Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282). In Dagstuhl Manifestos, Volume 10, Issue 1, pp. 1-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{delgrande_et_al:DagMan.10.1.1,
  author =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  title =	{{Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)}},
  pages =	{1--61},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2024},
  volume =	{10},
  number =	{1},
  editor =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.10.1.1},
  URN =		{urn:nbn:de:0030-drops-201403},
  doi =		{10.4230/DagMan.10.1.1},
  annote =	{Keywords: Knowledge representation and reasoning, Applications of logics, Declarative representations, Formal logic}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.AIB.2022
OASIcs, Volume 99, AIB 2022, Complete Volume

Authors: Camille Bourgaux, Ana Ozaki, and Rafael Peñaloza

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
OASIcs, Volume 99, AIB 2022, Complete Volume
Cite as

International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 1-180, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Proceedings{bourgaux_et_al:OASIcs.AIB.2022,
  title =	{{OASIcs, Volume 99, AIB 2022, Complete Volume}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{1--180},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022},
  URN =		{urn:nbn:de:0030-drops-159976},
  doi =		{10.4230/OASIcs.AIB.2022},
  annote =	{Keywords: OASIcs, Volume 99, AIB 2022, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.AIB.2022.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Camille Bourgaux, Ana Ozaki, and Rafael Peñaloza

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{bourgaux_et_al:OASIcs.AIB.2022.0,
  author =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{0:i--0:x},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.0},
  URN =		{urn:nbn:de:0030-drops-159984},
  doi =		{10.4230/OASIcs.AIB.2022.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.1
Knowledge Graphs: A Guided Tour (Invited Paper)

Authors: Aidan Hogan

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Much has been written about knowledge graphs in the past years by authors coming from diverse communities. The goal of these lecture notes is to provide a guided tour to the secondary and tertiary literature concerning knowledge graphs where the reader can learn more about particular topics. In particular, we collate together brief summaries of relevant books, book collections, book chapters, journal articles and other publications that provide introductions, primers, surveys and perspectives regarding: knowledge graphs in general; graph data models and query languages; semantics in the form of graph schemata, ontologies and rules; graph theory, algorithms and analytics; graph learning, in the form of knowledge graph embeddings and graph neural networks; and the knowledge graph life-cycle, which incorporates works on constructing, refining and publishing knowledge graphs. Where available, we highlight and provide direct links to open access literature.
Cite as

Aidan Hogan. Knowledge Graphs: A Guided Tour (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 1:1-1:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{hogan:OASIcs.AIB.2022.1,
  author =	{Hogan, Aidan},
  title =	{{Knowledge Graphs: A Guided Tour}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{1:1--1:21},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.1},
  URN =		{urn:nbn:de:0030-drops-159999},
  doi =		{10.4230/OASIcs.AIB.2022.1},
  annote =	{Keywords: knowledge graphs}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.2
Reasoning in Knowledge Graphs (Invited Paper)

Authors: Ricardo Guimarães and Ana Ozaki

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Knowledge Graphs (KGs) are becoming increasingly popular in the industry and academia. They can be represented as labelled graphs conveying structured knowledge in a domain of interest, where nodes and edges are enriched with metaknowledge such as time validity, provenance, language, among others. Once the data is structured as a labelled graph one can apply reasoning techniques to extract relevant and insightful information. We provide an overview of deductive and inductive reasoning approaches for reasoning in KGs.
Cite as

Ricardo Guimarães and Ana Ozaki. Reasoning in Knowledge Graphs (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 2:1-2:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{guimaraes_et_al:OASIcs.AIB.2022.2,
  author =	{Guimar\~{a}es, Ricardo and Ozaki, Ana},
  title =	{{Reasoning in Knowledge Graphs}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{2:1--2:31},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.2},
  URN =		{urn:nbn:de:0030-drops-160005},
  doi =		{10.4230/OASIcs.AIB.2022.2},
  annote =	{Keywords: Knowledge Graphs, Description Logics, Knowledge Graph Embeddings}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.3
Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces (Invited Paper)

Authors: Zied Bouraoui, Víctor Gutiérrez-Basulto, and Steven Schockaert

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Ontologies and vector space embeddings are among the most popular frameworks for encoding conceptual knowledge. Ontologies excel at capturing the logical dependencies between concepts in a precise and clearly defined way. Vector space embeddings excel at modelling similarity and analogy. Given these complementary strengths, there is a clear need for frameworks that can combine the best of both worlds. In this paper, we present an overview of our recent work in this area. We first discuss the theory of conceptual spaces, which was proposed in the 1990s by Gärdenfors as an intermediate representation layer in between embeddings and symbolic knowledge bases. We particularly focus on a number of recent strategies for learning conceptual space representations from data. Next, building on the idea of conceptual spaces, we discuss approaches where relational knowledge is modelled in terms of geometric constraints. Such approaches aim at a tight integration of symbolic and geometric representations, which unfortunately comes with a number of limitations. For this reason, we finally also discuss methods in which similarity, and other forms of conceptual relatedness, are derived from vector space embeddings and subsequently used to support flexible forms of reasoning with ontologies, thus enabling a looser integration between embeddings and symbolic knowledge.
Cite as

Zied Bouraoui, Víctor Gutiérrez-Basulto, and Steven Schockaert. Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 3:1-3:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{bouraoui_et_al:OASIcs.AIB.2022.3,
  author =	{Bouraoui, Zied and Guti\'{e}rrez-Basulto, V{\'\i}ctor and Schockaert, Steven},
  title =	{{Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{3:1--3:30},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.3},
  URN =		{urn:nbn:de:0030-drops-160015},
  doi =		{10.4230/OASIcs.AIB.2022.3},
  annote =	{Keywords: Conceptual Spaces, Ontologies, Vector Space Embeddings, Learning and Reasoning}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.4
Combining Embeddings and Rules for Fact Prediction (Invited Paper)

Authors: Armand Boschin, Nitisha Jain, Gurami Keretchashvili, and Fabian Suchanek

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Knowledge bases are typically incomplete, meaning that they are missing information that we would expect to be there. Recent years have seen two main approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as "If two people are married, they most likely live in the same city". These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of neuro-symbolic works that combine embeddings and rule mining approaches for fact prediction.
Cite as

Armand Boschin, Nitisha Jain, Gurami Keretchashvili, and Fabian Suchanek. Combining Embeddings and Rules for Fact Prediction (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 4:1-4:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{boschin_et_al:OASIcs.AIB.2022.4,
  author =	{Boschin, Armand and Jain, Nitisha and Keretchashvili, Gurami and Suchanek, Fabian},
  title =	{{Combining Embeddings and Rules for Fact Prediction}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{4:1--4:30},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.4},
  URN =		{urn:nbn:de:0030-drops-160021},
  doi =		{10.4230/OASIcs.AIB.2022.4},
  annote =	{Keywords: Rule Mining, Embeddings, Knowledge Bases, Deep Learning}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.5
Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper)

Authors: Manfred Jaeger

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling tool for graph and network data. Though much of the work on GNNs has focused on graphs with a single edge relation, they have also been adapted to multi-relational graphs, including knowledge graphs. In such multi-relational domains, the objectives and possible applications of GNNs become quite similar to what for many years has been investigated and developed in the field of statistical relational learning (SRL). This article first gives a brief overview of the main features of GNN and SRL approaches to learning and reasoning with graph data. It analyzes then in more detail their commonalities and differences with respect to semantics, representation, parameterization, interpretability, and flexibility. A particular focus will be on relational Bayesian networks (RBNs) as the SRL framework that is most closely related to GNNs. We show how common GNN architectures can be directly encoded as RBNs, thus enabling the direct integration of "low level" neural model components with the "high level" symbolic representation and flexible inference capabilities of SRL.
Cite as

Manfred Jaeger. Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 5:1-5:42, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{jaeger:OASIcs.AIB.2022.5,
  author =	{Jaeger, Manfred},
  title =	{{Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{5:1--5:42},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.5},
  URN =		{urn:nbn:de:0030-drops-160035},
  doi =		{10.4230/OASIcs.AIB.2022.5},
  annote =	{Keywords: Graph neural networks, Statistical relational learning}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.6
Automating Moral Reasoning (Invited Paper)

Authors: Marija Slavkovik

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Artificial Intelligence ethics is concerned with ensuring a nonnegative ethical impact of researching, developing, deploying and using AI systems. One way to accomplish that is to enable those AI systems to make moral decisions in ethically sensitive situations, i.e., automate moral reasoning. Machine ethics is an interdisciplinary research area that is concerned with the problem of automating moral reasoning. This tutorial presents the problem of making moral decisions and gives a general overview of how a computational agent can be constructed to make moral decisions. The tutorial is aimed for students in artificial intelligence who are interested in acquiring a starting understanding of the basic concepts and a gateway to the literature in machine ethics.
Cite as

Marija Slavkovik. Automating Moral Reasoning (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 6:1-6:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{slavkovik:OASIcs.AIB.2022.6,
  author =	{Slavkovik, Marija},
  title =	{{Automating Moral Reasoning}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{6:1--6:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.6},
  URN =		{urn:nbn:de:0030-drops-160043},
  doi =		{10.4230/OASIcs.AIB.2022.6},
  annote =	{Keywords: Machine ethics, artificial morality, artificial moral agents}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.TIME.2020
LIPIcs, Volume 178, TIME 2020, Complete Volume

Authors: Emilio Muñoz-Velasco, Ana Ozaki, and Martin Theobald

Published in: LIPIcs, Volume 178, 27th International Symposium on Temporal Representation and Reasoning (TIME 2020)

Abstract
LIPIcs, Volume 178, TIME 2020, Complete Volume
Cite as

27th International Symposium on Temporal Representation and Reasoning (TIME 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 178, pp. 1-292, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

Copy BibTex To Clipboard

@Proceedings{munozvelasco_et_al:LIPIcs.TIME.2020,
  title =	{{LIPIcs, Volume 178, TIME 2020, Complete Volume}},
  booktitle =	{27th International Symposium on Temporal Representation and Reasoning (TIME 2020)},
  pages =	{1--292},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-167-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{178},
  editor =	{Mu\~{n}oz-Velasco, Emilio and Ozaki, Ana and Theobald, Martin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2020},
  URN =		{urn:nbn:de:0030-drops-129670},
  doi =		{10.4230/LIPIcs.TIME.2020},
  annote =	{Keywords: LIPIcs, Volume 178, TIME 2020, Complete Volume}
}
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