Dagstuhl Seminar 23442
Approaches and Applications of Inductive Programming
( Oct 29 – Nov 03, 2023 )
Permalink
Please use the following short url to reference this page:
https://www.dagstuhl.de/23442
Organizers
- Andrew Cropper (University of Oxford, GB)
- Luc De Raedt (KU Leuven, BE)
- Richard Evans (DeepMind - London, GB)
- Ute Schmid (Universität Bamberg, DE)
Contact
- Michael Gerke (for scientific matters)
- Jutka Gasiorowski (for administrative matters)
Dagstuhl Seminar Wiki
- Dagstuhl Seminar Wiki (Use personal credentials as created in DOOR to log in)
Shared Documents
- Dagstuhl Materials Page (Use personal credentials as created in DOOR to log in)
Schedule
- Upload (Use personal credentials as created in DOOR to log in)
The goal of Inductive Programming (IP), also called inductive program synthesis, is to learn computer programs from data. IP is a special case of induction addressing the automated or semi-automated generation of a computer program from incomplete information, such as input-output examples, demonstrations (aka programming by example), or computation traces. Mostly, declarative (logic or functional) programs are synthesized and learned programs are often recursive. Examples are learning list manipulation programs, learning strategies for game playing, or learning constraints for scheduling problems. The goal of IP is to induce computer programs from data. 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 for programming education. IP is also of relevance for researchers in industry, providing tools for end-user programming such as the Microsoft Excel plug-in FlashFill.
Focus topics of the planned seminar will be on different aspects of neuro-symbolic approaches for IP, especially:
- Bringing together learning and reasoning,
- IP as a post-hoc approach to explaining decision-making of deep learning blackbox models, and
- exploring the potential of deep learning approaches, especially large language models such as OpenAI Codex for IP.
Furthermore, interactive approaches of IP will be discussed together with recent research on machine teaching. Potential applications of such approaches to end-user programming, as well as programming education will be explored based on cognitive science research on concept acquisition and human teaching.
Participants are encouraged to upload information about their research interests and topics they want to discuss before the seminar starts and also to browse the information offered by the other participants beforehand. The seminar is the sixth in a series which has started in 2013. A long-term objective of the seminar is to establish IP as a self-contained research topic in AI, especially as a field of ML and cognitive modelling. The seminar serves as a community-building event by bringing together researchers from different areas of IP, from different application areas such as end-user programming and tutoring and cognitive science research, especially from cognitive models of inductive (concept) learning. For successful community building, we seek to balance junior and senior researchers and to mix researchers from universities and industry.
Creative Commons BY 4.0
Andrew Cropper, Luc De Raedt, Richard Evans, and Ute Schmid
- Lun Ai (Imperial College London, GB)
- James Ainooson (Vanderbilt University - Nashville, US)
- Martin Berger (University of Sussex - Brighton, GB)
- David Cerna (The Czech Academy of Sciences - Prague, CZ)
- David J. Crandall (Indiana University - Bloomington, US)
- Andrew Cropper (University of Oxford, GB) [dblp]
- Luc De Raedt (KU Leuven, BE) [dblp]
- Sebastijan Dumancic (TU Delft, NL)
- Kevin Ellis (Cornell University - Ithaca, US) [dblp]
- Richard Evans (DeepMind - London, GB) [dblp]
- Nathanaël Fijalkow (CNRS - Talence, FR) [dblp]
- Bettina Finzel (Universität Bamberg, DE)
- Johannes Fürnkranz (Johannes Kepler Universität Linz, AT) [dblp]
- Céline Hocquette (University of Oxford, GB) [dblp]
- Frank Jäkel (TU Darmstadt, DE) [dblp]
- Emanuel Kitzelmann (Technische Hochschule Brandenburg, DE) [dblp]
- Tomáš Kliegr (University of Economics - Prague, CZ) [dblp]
- Maithilee Kunda (Vanderbilt University - Nashville, US)
- Johannes Langer (Universität Bamberg, DE)
- Pasquale Minervini (University of Edinburgh, GB)
- Sriraam Natarajan (University of Texas at Dallas - Richardson, US) [dblp]
- Stassa Patsantzis (University of Surrey - Guildford, GB)
- Josh Rule (University of California - Berkeley, US)
- Zeynep G. Saribatur (TU Wien, AT)
- Ute Schmid (Universität Bamberg, DE) [dblp]
- Gust Verbruggen (Microsoft - Keerbergen, BE) [dblp]
- Felix Weitkämper (LMU München, DE)
Related Seminars
- Dagstuhl Seminar 13502: Approaches and Applications of Inductive Programming (2013-12-08 - 2013-12-11) (Details)
- Dagstuhl Seminar 15442: Approaches and Applications of Inductive Programming (2015-10-25 - 2015-10-30) (Details)
- Dagstuhl Seminar 17382: Approaches and Applications of Inductive Programming (2017-09-17 - 2017-09-20) (Details)
- Dagstuhl Seminar 19202: Approaches and Applications of Inductive Programming (2019-05-12 - 2019-05-17) (Details)
- Dagstuhl Seminar 21192: Approaches and Applications of Inductive Programming (2021-05-09 - 2021-05-12) (Details)
Classification
- Artificial Intelligence
- Human-Computer Interaction
- Machine Learning
Keywords
- Interpretable Machine Learning
- Neuro-symbolic AI
- Explainable AI
- Human-like Machine Learning
- Inductive Logic Programming
