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Documents authored by Evans, Richard

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.
Cite as

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}
}
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