Research interests
I am interested in highly efficient constraint programming, developing new constraint
solving algorithms and implementations, and in modelling and solving problems using
constraint programming.
Most of my work is with the Minion solver project,
the Dominion constraint solver synthesizer project,
and the Savile Row constraint modelling tool.
Previously I have researched quantified constraint programming (QCSP) and its applications.
My Ph.D. thesis on this topic can be found here.
I developed algorithms
(mainly propagation algorithms) for nonbinary QCSP, and modelled a factory scheduling
problem with uncertainty in QCSP.
Publications
Journal papers
Ian P. Gent, Christopher Jefferson, Steve Linton, Ian Miguel, Peter Nightingale,
Generating Custom Propagators for Arbitrary Constraints,
Artificial Intelligence, Volume 211, pages 133, doi: 10.1016/j.artint.2014.03.001, 2014.
Thomas W. Kelsey, Lars Kotthoff, Christopher A. Jefferson, Stephen A. Linton, Ian Miguel, Peter Nightingale, and Ian P. Gent,
Qualitative Modelling via Constraint Programming,
Constraints (Special issue on future directions for constraint programming), Volume 19, pages 163173, doi: 10.1007/s1060101491586, 2014.
Peter Nightingale, Ian P. Gent, Christopher Jefferson, Ian Miguel,
Short and Long Supports for Constraint Propagation,
Journal of Artificial Intelligence Research, Volume 46, pages 145, 2013.
Peter Nightingale,
The Extended Global Cardinality Constraint: An Empirical
Survey,
Artificial Intelligence, Volume 175 issue 2, pages 586614,
doi:10.1016/j.artint.2010.10.005, 2011.
Christopher Jefferson, Neil Moore, Peter Nightingale, Karen E. Petrie,
Implementing Logical Connectives in Constraint Programming,
Artificial Intelligence, Volume 174, pages 14071429, 2010.
Peter Nightingale,
Nonbinary Quantified CSP: Algorithms and Modelling,
Constraints, Volume 14, pages 539581, 2009.
Ian P. Gent, Ian Miguel and Peter Nightingale,
Generalised Arc Consistency for the AllDifferent Constraint: An Empirical Survey,
Artificial Intelligence, Volume 172 number 18, pages 19732000, 2008.
Unfortunately this paper contains an error in one of the pseudocode algorithms. Errata
Ian P. Gent, Peter Nightingale, Andrew Rowley and Kostas Stergiou,
Solving Quantified Constraint Satisfaction Problems,
Artificial Intelligence, Volume 172, pages 738–771, 2008.
Ian P. Gent, Christopher Jefferson, Tom Kelsey, Inês Lynce, Ian Miguel, Peter Nightingale, Barbara M. Smith
and S. Armagan Tarim
Search in the Patience Game "Black Hole",
AI Communications, Volume 20, Number 3, pages 211226, 2007.
Alan M. Frisch, Timothy J. Peugniez, Anthony J. Doggett and Peter Nightingale,
Solving NonBoolean Satisfiability Problems with Stochastic Local Search: A Comparison of Encodings,
Journal of Automated Reasoning, Volume 35, pages 143179, 2005.
Iain Bate, John McDermid and Peter Nightingale,
Establishing Timing Requirements for Control Loops in RealTime Systems,
Journal of Microprocessors and Microsystems, 27(4), 159169, 2003.
Conference papers
Peter Nightingale, Ozgur Akgun, Ian P. Gent, Christopher Jefferson, and Ian Miguel,
Automatically Improving Constraint Models in Savile Row through AssociativeCommutative Common Subexpression Elimination
(slides),
in Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming (CP), 2014.
Ian P. Gent, Bilal Syed Hussain, Christopher Jefferson, Lars Kotthoff, Ian Miguel, Glenna F. Nightingale, and Peter Nightingale,
Discriminating Instance Generation for Automated Constraint Model Selection,
in Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming (CP), 2014.
Ozgur Akgun, Ian P. Gent, Christopher Jefferson, Ian Miguel and Peter Nightingale,
Breaking Conditional Symmetry in Automated Constraint Modelling with Conjure,
in Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 2014.
Ozgur Akgun, Alan M. Frisch, Ian P. Gent, Bilal Syed Hussain, Christopher Jefferson, Lars Kotthoff, Ian Miguel and Peter Nightingale,
Automated Symmetry Breaking and Model Selection in Conjure,
in Proceedings of the 19th International Conference on Principles and Practice of Constraint Programming (CP), pages 107116, 2013.
Christopher Jefferson and Peter Nightingale,
Extending Simple Tabular Reduction with Short Supports
(slides, poster),
in Proceedings of 23nd International Joint Conference on Artificial Intelligence (IJCAI), pages 573579, 2013.
Dharini Balasubramaniam, Chris Jefferson, Lars Kotthoff, Ian Miguel, Peter Nightingale,
An automated approach to generating efficient constraint solvers,
in Proceedings of 34th International Conference on Software Engineering (ICSE), pages 661671, 2012.
Peter Nightingale, Ian P. Gent, Chris Jefferson and Ian Miguel,
Exploiting Short Supports for Generalised Arc Consistency for Arbitrary Constraints,
(slides, poster)
in Proceedings of 22nd International Joint Conference on Artificial Intelligence (IJCAI), pages 623628, 2011.
Dharini Balasubramaniam, Lakshitha de Silva, Chris Jefferson, Lars Kotthoff, Ian Miguel and Peter Nightingale,
Dominion: An Architecturedriven Approach to Generating Efficient Constraint Solvers,
in Proceedings of 9th Working IEEE/IFIP Conference on Software Architecture (WICSA), pages 228231, 2011.
Ian P. Gent, Chris Jefferson, Ian Miguel, and Peter Nightingale,
Generating Specialpurpose Stateless Propagators for Arbitrary Constraints,
in Proceedings of 16th International Conference on Principles and Practice of Constraint Programming (CP 2010), pages 206220, 2010.
Lars Kotthoff, Ian Miguel and Peter Nightingale,
Ensemble classification for constraint solver configuration,
in Proceedings of 16th International Conference on Principles and Practice of Constraint Programming (CP 2010), pages 321329, 2010.
Ian P. Gent, Lars Kotthoff, Ian Miguel, Neil C.A. Moore, Peter Nightingale and Karen E. Petrie,
Learning When to Use Lazy Learning in Constraint Solving,
in Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010), pages 873878, 2010.
Sophie Huczynska, Paul McKay, Ian Miguel and Peter Nightingale,
Modelling Equidistant Frequency Permutation Arrays: An Application of Constraints to Mathematics,
(slides)
in Proceedings of Principles and Practice of Constraint Programming (CP 2009), 5064, 2009.
Ian P. Gent, Christopher Jefferson, Ian Miguel and Peter Nightingale,
Data Structures for Generalised Arc Consistency for Extensional Constraints, (slides)
in Proceedings of the Twenty Second Conference on Artificial Intelligence (AAAI07), 191197, 2007.
Ian P. Gent, Peter Nightingale and Kostas Stergiou,
QCSPSolve: A Solver for Quantified Constraint Satisfaction Problems,
in Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), 2005.
Peter Nightingale,
Consistency for Quantified Constraint Satisfaction Problems,
Poster and short paper in Proceedings of the 11th International Conference on
Principles and Practice of Constraint Programming (CP 2005), pages 792796, 2005.
Ian P. Gent, Peter Nightingale and Andrew Rowley,
Encoding Quantified CSPs as Quantified Boolean Formulae,
(slides)
in Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), pages 176180, 2004.
Iain Bate, Peter Nightingale and Anton Cervin,
Establishing Timing Requirements and Control Attributes for Control Loops in RealTime Systems,
in Proceedings of the 15th Euromicro Conference on RealTime Systems, 121128, 2003.
Abstracts and Invited Talks
Automated Reformulation of Constraint Models in Savile Row, tutorial
presented at CP 2014.
The Extended Global Cardinality Constraint: An Empirical Survey: Extended Abstract
(slides, poster),
in Proceedings of 23nd International Joint Conference on Artificial Intelligence (IJCAI), 2013.
This is an abstract of the Artificial Intelligence Journal paper, presented in the IJCAI 2013 Journal Track.
Watched Literals and Generating Propagators in Constraint Programming,
21st International Symposium on Mathematical Programming (ISMP 2012).
For slides click here.
The Alldifferent Constraint: Efficiency Measures,
ACP Summer School 2008.
For slides click here.
Workshop papers
Ian P. Gent, Chris Jefferson, Ian Miguel, Neil C.A. Moore, Peter Nightingale, Patrick Prosser and Chris Unsworth,
A Preliminary Review of Literature on Parallel Constraint Solving,
to appear in Proceedings PMCS’11 Workshop on Parallel Methods for Constraint Solving, September 2011.
Ian Gent, Lars Kotthoff, Ian Miguel and Peter Nightingale,
Machine learning for constraint solver design — a case study for the alldifferent
constraint,
in Proceedings of the 3rd Workshop on Techniques for Implementing Constraint Programming Systems (TRICS), St Andrews, Scotland, September 2010.
Ian P. Gent, Paul McKay, Ian Miguel, Peter Nightingale and Sophie Huczynska,
Modelling Equidistant Frequency Permutation Arrays in Constraints,
in Proceedings of the Eighth Symposium on Abstraction, Reformulation and Approximation (SARA 2009).
This paper is superseded by Modelling Equidistant Frequency Permutation Arrays: An Application of Constraints to Mathematics (CP 2009).
Peter Nightingale,
Consistency for Quantified Constraint Satisfaction Problems,
(slides)
in Proceedings of 1st Workshop on Quantification in Constraint Programming, Kostas Stergiou (ed), 2005.
Ian P. Gent and Peter Nightingale,
A New Encoding of AllDifferent into SAT,
in Proceedings 3rd International Workshop on Modelling and
Reformulating Constraint Satisfaction Problems, CP2004, Toronto, Canada, Frisch, AM, Miguel, I (eds), pages 95110, 2004.
Thesis
Peter Nightingale, Consistency and the Quantified Constraint Satisfaction Problem,
PhD thesis, University of St Andrews, 2007. Available from the University of St Andrews repository and also here. Please use this URL to cite: http://hdl.handle.net/10023/759
Software
Savile Row  With Ian Miguel I wrote Savile Row, a tool for
translating the Essence' modelling language to the input languages of constraint solvers (currently
Minion is the main target).
Minion  I wrote the network flow propagators for Minion, and they are described in papers
Generalised Arc Consistency for the AllDifferent Constraint: An Empirical Survey and The Extended Global Cardinality Constraint: An Empirical
Survey.
Dominion is a constraint solver synthesizer  given a
particular problem class or instance, it can create a constraint solver specifically for that class or instance by
assembling a library of components.
Queso is a nonbinary QCSP solver written in Java for my PhD. The source code is available on the
basis that it is unsupported, but I may be able to help with some simple problems. (There is a timing
component written in C for Linux, but this can probably be easily removed if you wish to run it on
other systems.)
queso15908.tgz
Activities
PC member for AAAI 2011  25th Conference on Artificial Intelligence.
PC member for CP 2011  17th International Conference on Principles and Practice of Constraint Programming.
PC member for CP 2010  16th International Conference on Principles and Practice of Constraint Programming.
Chair (with Standa Živný) of the CP 2010 Doctoral Programme.
Chair (with Chris Jefferson and Guido Tack) of TRICS 2010  3rd workshop on
Techniques foR Implementing Constraint programming Systems
Publicity for CSCLP 2011, Annual ERCIM
Workshop on Constraint Solving and Constraint Logic Programming.
