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Open Access research that better understands changing marine ecologies...

Strathprints makes available scholarly Open Access content by researchers in the Department of Mathematics & Statistics.

Mathematics & Statistics hosts the Marine Population Modelling group which is engaged in research into topics surrounding marine resource modelling and ecology. Recent work has included important developments in the population modelling of marine species.

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Jump to: 2023 | 2022
Number of items: 5.

2023

Murray, Andrew and Arulselvan, Ashwin and Roper, Marc and Cashmore, Michael and Mohalik, Swarup Kumar and Burdick, Ian and David, Sushanth; (2023) The cost of quality of service : SLA aware VNF placement and routing using column generation. In: Proceedings of 2023 13th International Workshop on Resilient Networks Design and Modeling (RNDM). International Workshop on Resilient Networks Design and Modeling (RNDM) . IEEE, DEU. ISBN 9798350327359

Murray, Andrew and Arulselvan, Ashwin and Cashmore, Michael and Roper, Marc and Frank, Jeremy; Koenig, Sven and Stern, Roni and Vallati, Mauro, eds. (2023) A column generation approach to correlated simple temporal networks. In: Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling. Proceedings of the International Conference on Automated Planning and Scheduling . AAAI Press, CZE, pp. 295-303. ISBN 9781577358817

2022

Murray, Andrew and Cashmore, Michael and Arulselvan, Ashwin and Frank, Jeremy; (2022) Joint chance constrained probabilistic simple temporal networks via column generation. In: 15th International Symposium on Combinatorial Search. Association for the Advancement of Artificial Intelligence, California, USA, pp. 305-307. ISBN 1577358732

Murray, Andrew and Cashmore, Michael and Arulselvan, Ashwin (2022) Joint chance constrained probabilistic simple temporal networks via column generation (extended abstract). In: 19th International Conference on the Integration of Constraint Programmng, Artificial Intelligence, and Operations Research, 2022-06-20 - 2022-06-23, https://sites.google.com/usc.edu/cpaior-2022.

Murray, Andrew and Krarup, Benjamin and Cashmore, Michael; Bapi, Raju and Kulkarni, Sandeep and Mohalik, Swarup and Peri, Sathya, eds. (2022) Towards temporally uncertain explainable AI planning. In: Distributed Computing and Intelligent Technology - 18th International Conference, ICDCIT 2022, Lecture Notes in Computer Science. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13145 . Springer Science and Business Media Deutschland GmbH, IND, pp. 45-59. ISBN 9783030948757

This list was generated on Tue Jul 16 13:01:49 2024 BST.