Prof Ian Miguel

Head of School

Researcher profile

Phone

+44 (0)1334 46 3248

Email

ijm@st-andrews.ac.uk

Office

C0.13

Location

Jack Cole Building (Computer Science )

 

Biography

I am a Professor and Head of School of the School of Computer Science at St Andrews, which I joined the in 2004.

I have worked in Artificial Intelligence for over 25 years, specialising in solving combinatorial optimisation problems through Constraint Programming and related technologies.

Teaching

I presently teach on:

• CS4402: Constraint Programming
• CS4303: Video Games

Research areas

I work in Artificial Intelligence, specifically in solving complex combinatorial optimisation problems, such as planning, scheduling, or routing with technologies such as Constraint Programming or Propositional Satisfiability (SAT).

Much of our work in St Andrews has focused on the important problem of modelling. A model in this sense is the description of the problem we wish to solve suitable for input to an automated solver - the quality of the model has a very significant impact on solving performance. We have developed a Constraint Modelling Pipeline to automate this modelling process, compiling a high-level description of a problem down to a variety of powerful solving technologies.

PhD supervision

• Erdem Kus

Selected publications

• Open access

  A framework for generating informative benchmark instances

  Dang, N., Akgun, O., Espasa Arxer, J., Miguel, I. J. & Nightingale, P., 23 Jul 2022, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Solon, C. (ed.). Dagstuhl: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 18 p. 18. (Leibniz International Proceedings in Informatics (LIPIcs); vol. 235).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Automatic tabulation in constraint models

  Akgün, Ö., Gent, I. P., Jefferson, C., Kiziltan, Z., Miguel, I., Nightingale, P., Salamon, A. Z. & Ulrich-Oltean, F., 26 Feb 2022, (Submitted) 51 p.

  Research output: Working paper › Preprint

• Open access

  CONJURE: automatic generation of constraint models from problem specifications

  Akgun, O., Frisch, A. M., Gent, I. P., Jefferson, C., Miguel, I. J. & Nightingale, P., Sep 2022, In: Artificial Intelligence. 310, 27 p., 103751.

  Research output: Contribution to journal › Article › peer-review

• Open access

  Plotting: a planning problem with complex transitions

  Espasa Arxer, J., Miguel, I. J. & Villaret, M., 23 Jul 2022, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Solon, C. (ed.). Dagstuhl: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 17 p. 23. (Leibniz International Proceedings in Informatics (LIPIcs); vol. 235).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• A preliminary Case Study of Planning With Complex Transitions: Plotting

  Espasa Arxer, J., Miguel, I. J., Coll, J. & Villaret, M., 25 Oct 2021.

  Research output: Contribution to conference › Paper › peer-review

• Open access

  Exploring lifted planning encodings in Essence Prime

  Espasa, J., Coll, J., Miguel, I. & Villaret, M., 14 Oct 2021, Artificial Intelligence Research and Development: Proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence. Villaret, M., Alsinet, T., Fernández, C. & Valls, A. (eds.). IOS Press, p. 66-75 (Frontiers in Artificial Intelligence and Applications; vol. 339).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Open access

  Towards reformulating Essence specifications for robustness

  Akgün, Ö., Frisch, A. M., Gent, I. P., Jefferson, C., Miguel, I., Nightingale, P. & Salamon, A. Z., 25 Oct 2021, ModRef 2021 - The 20th workshop on Constraint Modelling and Reformulation (ModRef). 12 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Open access

  Discriminating instance generation from abstract specifications: a case study with CP and MIP

  Akgün, Ö., Dang, N., Miguel, I., Salamon, A. Z., Spracklen, P. & Stone, C., 2020, Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020, Vienna, Austria, September 21–24, 2020, Proceedings. Hebrard, E. & Musliu, N. (eds.). Cham: Springer, p. 41-51 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12296 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Open access

  Efficient incremental modelling and solving

  Koçak, G., Akgün, Ö., Dang, N. & Miguel, I., 7 Sep 2020, ModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation. 15 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Open access

  Exploiting incomparability in solution dominance: improving general purpose constraint-based mining

  Kocak, G., Akgun, O., Guns, T. & Miguel, I. J., 29 Aug 2020, ECAI 2020: 24th European Conference on Artificial Intelligence. De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarín, A. & Lang, J. (eds.). Amsterdam: IOS Press, p. 331-338 8 p. (Frontiers in artificial intelligence and applications; vol. 325).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

 

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