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10th International Workshop on Approaches and Applications of Inductive Programming, IP2020-21 @ IJCLR
The AAIP workshop series, started in 2005, is a bi-annual event aiming at promoting research in Inductive programming (IP), a field of machine learning concerned with learning executable programs in arbitrary programming languages, from incomplete specifications, typically input/output examples. IP approaches include Inductive Logic Programming (ILP) and Inductive Functional Programming and can be considered as highly expressive approaches to interpretable machine learning. IP is an important research direction for machine learning and artificial intelligence in general, since the general program synthesis task calls for approaches that go beyond the requirements of algorithms for concept learning, addressing learning (recursive) rules from experience. Pushing research forward in this area can give important insights in the nature and complexity of learning as well as enlarging the field of possible applications, which currently include software engineering, language learning, AI-planning, as well as cognitive aspects of learning.


AAIP Invited Speaker

TUE Sept. 26, 16:00








Armando Solar-Lezama
Computer Science & Artificial Intelligence Laboratory, MIT

Title: Neurosymbolic Reasoning for Better Learning

Bio: Prof. Armando Solar-Lezama is a Professor at MIT, where he leads the Computer Assisted Programming group and is also Associate Director and COO of the Computer Science and Artificial Intelligence Lab (CSAIL). He also leads the NSF funded Expeditions in Computing Project “Understanding the World Through Code”, which aims to develop neurosimbolic learning techniques for the purpose of accelerating scientific discovery.


AAIP program

Program


Call for Papers

We invite theoretical and applied submissions, as well as reports of latest and ongoing research, on all areas related to Inductive Programming. Topics of interest include, but are not limited to:

  • Inductive methods for program synthesis.
  • End-user programming.
  • Example-driven programming.
  • Schema-guided program induction.
  • Probabilistic programming.
  • IP as surrogate models for deep learning.
  • Human-like rule learning.
  • Explanation generation from IP-learned rule sets.
  • Comparing IP approaches with other rule-learning approaches.
  • Combining logic and functional program induction.
  • IP applications.


Program Committee Chairs

Ute Schmid

Cèsar Ferri Ramirez


Program Committee Members

Javier Segovia-Aguas, (Universitat Pompeu Fabra, Barcelona, Spain)

François Chollet (Google, Mountain View, CA)

Andrew Cropper (University of Oxford, GB)

Richard Evans (Google DeepMind – London, GB)

Johannes Fürnkranz (TU Darmstadt, DE)

José Hernández-Orallo (Technical University of Valencia, ES)

Susumu Katayama University of Miyazaki, Japan)

Tomáš Kliegr (University of Economics – Prague, CZ)

Alex Polozov (Microsoft Corporation – Redmond, US)

Luc De Raedt (KU Leuven, BE)

Stephen H. Muggleton (Imperial College London, GB)

Rishabh Singh (Microsoft Research – Redmond, US)

Armando Solar-Lezama (MIT – Cambridge, US)

Ruzica Piskac (Yale University – New Haven, US)

Harald Ruess (fortiss GmbH – München, DE)

Paper Submission

Researchers and practitioners are invited to submit original papers that have not been submitted for review or published elsewhere. Submitted papers must be written in English, should be formatted using single column and 11pt font, and should not exceed 8 pages in the case of research and experience papers, or 4 pages in the case of position papers (including figures, bibliography and appendices). All submitted papers will be judged based on their relevance, originality, significance, technical quality and organisation.

Submissions will be handled by EasyChair. To submit a paper, authors are invited to follow the submission link and select the AAIP track.


Publication

All accepted papers will be published by CEUR and are expected to be presented at the workshop.


Journal Track

Authors are invited to submit high-quality work at IJCLR’s journal track on Learning & Reasoning, supported by the Machine Learning Journal. The upcoming cut-off date for the journal track is June 1 2021. Accepted papers will be presented to IJCLR 2021 and published at the Machine Learning Journal special issue on Learning & Reasoning. More details, including formatting and submission guidelines, may be found here.


Important dates

  • Deadline for paper submission: June 30 2021 July 7 2021 (extended)
  • Notification of paper acceptance: August 2021
  • Camera-ready due: September 2021
  • AAIP Workshop: October 25-27 2021

The deadline on each of these dates is midnight, Central European Summer Time (UTC + 2)


Previous Workshops

AAIP 2021 Dagstuhl Seminar 21192, Germany

AAIP 2019 Dagstuhl Seminar 19202, Germany

AAIP 2017 Dagstuhl Seminar 17382, Germany

AAIP 2015 Dagstuhl Seminar 15442, Germany

AAIP 2013 Dagstuhl Seminar 13502, Germany

AAIP 2011 co-located with PPDP 2011 and LOPSTR 2011, Odense, Denmark

AAIP 2009 at ICFP 2009 in Edinburgh, Scotland

AAIP 2007 at ECML 2007 in Warsaw, Poland

AAIP 2005 at ICML 2005 in Bonn, Germany