May 12 – 17 , 2019, Dagstuhl Seminar 19202

Approaches and Applications of Inductive Programming


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

For support, please contact

Dagstuhl Service Team


List of Participants
Shared Documents
Dagstuhl Seminar Schedule [pdf]


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.

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

Dagstuhl Seminar Series


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


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


In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.


Download overview leaflet (PDF).


Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.