Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192)

Authors Andrew Cropper, Luc De Raedt, Richard Evans, Ute Schmid and all authors of the abstracts in this report

Thumbnail PDF


  • Filesize: 1.81 MB
  • 14 pages

Document Identifiers

Author Details

Andrew Cropper
  • University of Oxford, GB
Luc De Raedt
  • KU Leuven, BE
Richard Evans
  • DeepMind - London, GB
Ute Schmid
  • Universität Bamberg, DE
and all authors of the abstracts in this report

Cite AsGet BibTex

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)


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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Machine learning
  • Software and its engineering → Compilers
  • Human-centered computing → Human computer interaction (HCI)
  • Interpretable Machine Learning
  • Explainable Artificial Intelligence
  • Interactive Learning
  • Human-like Computing
  • Inductive Logic Programming


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail