https://www.dagstuhl.de/19202

12. – 17. Mai 2019, Dagstuhl-Seminar 19202

Approaches and Applications of Inductive Programming

Organisatoren

Luc De Raedt (KU Leuven, BE)
Richard Evans (Google DeepMind – London, GB)
Stephen H. Muggleton (Imperial College London, GB)
Ute Schmid (Universität Bamberg, DE)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Michael Gerke zu administrativen Fragen

Shida Kunz zu wissenschaftlichen Fragen

Motivation

Inductive programming addresses the problem of learning programs from incomplete specifications - typically from input/output examples. Beginning in the 1960s, this area of research was initiated in artificial intelligence exploring the complex cognitive processes involved in producing program code which satisfies some specification. Inductive programming can be seen as a subdomain of machine learning where the hypothesis space consists of classes of computer programs. Researchers working on this topic have their background in diverse areas of computer science, namely in machine learning, artificial intelligence, declarative programming, program verification, and software engineering. Furthermore, inductive programming is of interest for researchers in cognitive science, working on computational models of inductive learning from experience, and for researchers in education, especially in intelligent tutoring. A break-through from basic research to applications for the mass-market was achieved by applying inductive programming techniques to programming by examples support of end-users for Microsoft Excel (Flashfill). This Dagstuhl Seminar is planned as a continuation of Dagstuhl Seminars 13502, 15442, and 17382. In the first seminar, focus was on exploring the different areas of basic research and applications of inductive programming. In the second seminar, in-depth coverage of algorithmic methods was provided and the relation to cognitive modeling was explored. In the third seminar focus was on different application areas such as data cleansing, teaching programming, and interactive training.

In the fourth seminar, focus will be on the potential of inductive programming

  • as an approach to explainable AI,
  • for support in data science,
  • in relation to neural computation, especially deep networks, and
  • as a special style of human-like computing.

License
  Creative Commons BY 3.0 DE
  Luc De Raedt, Richard Evans, Stephen H. Muggleton, and Ute Schmid

Dagstuhl-Seminar Series

Classification

  • Artificial Intelligence / Robotics
  • Programming Languages / Compiler
  • Society / Human-computer Interaction

Keywords

  • Inductive logic programming
  • Enduser programming
  • Probabilistic programming
  • Human-like computing
  • Explainable AI

Buchausstellung

Bücher der Teilnehmer 

Buchausstellung im Erdgeschoss der Bibliothek

(nur in der Veranstaltungswoche).

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

Dagstuhl's Impact

Bitte informieren Sie uns, wenn eine Veröffentlichung ausgehend von
Ihrem Seminar entsteht. Derartige Veröffentlichungen werden von uns in der Rubrik Dagstuhl's Impact separat aufgelistet  und im Erdgeschoss der Bibliothek präsentiert.