Professor Peter Stuckey

  • Room: Level: 06 Room: 6.19
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Combinatorial Optimization
  • Constraint Programming
  • Declarative Languages

Personal webpage

http://people.eng.unimelb.edu.au/pstuckey

Biography

Peter Stuckey is a Professor in the Department of Computing and Information Systems and NICTA Victoria Research Laboratory at The University of Melbourne.

Prof Stuckey is a pioneer in constraint programming, helping to develop the semantics and one of the earliest systems. He is involved in the G12 project, a software platform for solving large scale industrial combinatorial optimisation problems. The system uses Constraint Programming (CP) to allow problems to be stated simply, and then solved efficiently. Both solution development time and computing time and scalability can be dramatically reduced. Advanced software engineering is used to encapsulate algorithms from several different disciplines, so they can be reused and combined freely. Program development can be accelerated by mapping low level computation back to the problem model enabling the programmer to analyse and improve algorithm behaviour. This research will enable Australian industry to exploit resources more efficiently; it will support more efficient management of complex private and public utilities such as transportation, communication, power and water; and it will support optimal and justifiable strategic decision making and investment.

Recent publications

  1. Kafle B, Gange G, Schachte P, Sondergaard H, Stuckey P. A benders decomposition approach to deciding modular linear integer arithmetic. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10491 LNCS.
  2. Ganji M, Bailey J, Stuckey P. A declarative approach to constrained community detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10416 LNCS.
  3. Amadini R, Gange G, Stuckey P, Tack G. A novel approach to string constraint solving. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10416 LNCS.
  4. Davies T, Gange G, Stuckey P. Automatic logic-based benders decomposition with minizinc. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. 2017.
  5. Amadini R, Jordan A, Gange G, Gauthier F, Schachte P, Sondergaard H, Stuckey P, Zhang C. Combining string abstract domains for javascript analysis: An evaluation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10205 LNCS.
  6. Albert E, Arenas P, De La Banda MG, GÓmez-Zamalloa M, Stuckey P. Context-sensitive dynamic partial order reduction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10426 LNCS.
  7. Gange G, Ganty P, Stuckey P. Fixing the state budget: Approximation of regular languages with small DFAs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10482 LNCS.
  8. De UÑa D, Gange G, Schachte P, Stuckey P. Minimizing landscape resistance for habitat conservation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10335 LNCS.
  9. Amadini R, Flener P, Pearson J, Scott JD, Stuckey P, Tack G. MiniZinc with strings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10184 LNCS.
  10. Kreter S, Schutt A, Stuckey P, Zimmermann J. Mixed-integer linear programming and constraint programming formulations for solving resource availability cost problems. European Journal of Operational Research. Elsevier Science. 2017.
  11. Beldiceanu N, Carlsson M, Derrien A, Prud Homme C, Schutt A, Stuckey P. Range-consistent forbidden regions of allen’s relations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10335 LNCS.
  12. Blom M, Pearce A, Stuckey P. Short-term scheduling of an open-pit mine with multiple objectives. ENGINEERING OPTIMIZATION. Taylor & Francis Ltd. 2017, Vol. 49, Issue 5.
  13. Subramanian R, Allison L, Stuckey P, De La Banda MG, Abramson D, Lesk AM, Konagurthu AS. Statistical compression of protein folding patterns for inference of recurrent substructural themes. 2017 DATA COMPRESSION CONFERENCE (DCC). IEEE Computer Society. 2017. Editors: Bilgin A, Marcellin MW, Serrasagrista J, Storer JA.
  14. Collier JH, Allison L, Lesk AM, Stuckey P, De La Banda MG, Konagurthu AS. Statistical inference of protein structural alignments using information and compression. BIOINFORMATICS. Oxford University Press. 2017, Vol. 33, Issue 7.
  15. Kreter S, Schutt A, Stuckey P. Using constraint programming for solving RCPSP/max-cal. CONSTRAINTS. Springer. 2017, Vol. 22, Issue 3.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile