MINION was started as an academic research project, and we envision that it will continue to be used in a research context, as well as being of use to the wider CP community.

• The following paper discusses datastructures for the table constraint.

  Data Structures for Generalised Arc Consistency for Extensional Constraints
  Ian P. Gent, Chris Jefferson, Ian Miguel and Peter Nightingale.
  Proceedings of the Twenty Second Conference on Artificial Intelligence (to appear), 2007.

• Minion now includes a translator, TAILOR that converts high-level Essence' problem models to Minion format. Interesting features of this 'tailoring' process are discussed in the following paper.

  Tailoring Solver-independent Constraint Models: A Case Study with Essence' and Minion
  Andrea Rendl, Ian P. Gent and Ian Miguel, Proceedings of SARA 07.

• The following paper describes the design principles behind Minion. It was chosen as one of the ten best papers out of about 500 submissions for ECAI 06.

  MINION: A Fast, Scalable, Constraint Solver
  Ian P. Gent, Chris Jefferson and Ian Miguel.
  The European Conference on Artificial Intelligence 2006 (ECAI 06).
  Slides from the talk by Chris Jefferson are available.

• Minion features "watched literals", an implementation idea for propagation algorithms which has proved very successful in SAT. The following paper describes watched literals and other kinds of watched triggers for constraint propagation.

  Watched Literals for Constraint Propagation in Minion
  Ian P. Gent, Chris Jefferson, Ian Miguel. Proceedings, CP06.
  Slides from the talk by Chris Jefferson are available.

  Also available are Slides from Chris Jefferson's invited talk at the Constraint Propagation workshop at CP06.

As well as papers describing Minion, we would like to list related papers here by people who have used Minion, or evaluated it. If you have such a paper, please contact the project team with details.