Email: peter.stuckey@monash.edu.
Room: H6.39
Address: Woodside Technology and Design Building, 20 Exhibition Walk, Clayton VIC 3168, Monash Clayton Campus, Room 2.39
Phone: +613-9903-2405
Fax: +613-9903-1077

Peter Stuckey

Admin + Teaching

My teaching commitments for 2021 are

Research + Service

My research interests include constraint programming, logic programming, discrete optimization, combinatorial search, path planning and program analysis. Other things of interest:

Some invited talks

  • ICLP/CP 2005 "G12: From Solver Independent Models to Efficient Solutions" Powerpoint PDF
  • APLAS 2006 "Type Processing by Constraint Reasoning" PDF
  • Reformulation and Modelling Workshop CP2007 "Trials and Tribulations of Designing a Modelling Language" PDF
  • CPAIOR 2010 "Lazy Clause Generation: Combining the best of SAT and CP (and MIP?) solving" PDF
  • SAT 2013 "There are no CNF problems" PDF
  • CP 2013 "Those who forget the past are condemned to repeat it" PDF
  • PPDP 2013 "Search is dead, long live proof!" PDF
  • LaSH 2014 "Laziness is next to Godliness" PDF
  • AAMAS 2016 "Discrete Optimization for Agents" PDF
  • CPAIOR 2020/SOCS 2020 "Discrete Optimization for Multi-Agent Path Finding" PDF
  • LPOP 2020 "From CLP(R) to MiniZinc: There and Back Again" PDF

Results for Specific Problems

Next Generation Optimization

I lead the Monash Optimisation research group situated in the Department of Data Science and Artificial Intelligence. We are developing the next generation of discrete optimization technology:
  • High level modelling language MiniZinc allows concise and powerful modelling of problems, and solving of the same model by many solvers: constraint programming, mixed integer programming, SAT, SMT and local search.
  • Powerful solving technology lazy clause generation provides the state-of-the-art constraint programming solvers, and unbeatable results on many problems, particularly scheduling.
  • Nested constraint programming: an extension to CP to allow the expression and solving of complex nested discrete optimization problems, such as minimax problems, stochastic optimization problems, bi-level and multi-level optimization, quantified constraint optimization problems, and more.

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Created: 19 June 1995
Last modified 15th September 2021

Maintainer: Peter Stuckey,