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Ms Luke Kelly

Lecturer

Staff Member Details
Email: l.kelly@ecu.edu.au
ORCID iD: https://orcid.org/0000-0002-5194-3821

Luke is a Lecturer in the School of Science.

Qualifications

  • Bachelor of Computer Science Major in Games Programming, Edith Cowan University, 2016.

Research Outputs

2023

Journal Articles

  • Masek, M., Lam, P., Kelly, L., Wong, M. (2023). Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees. Annals of Operations Research, 320(2), 901-936. https://doi.org/10.1007/s10479-021-04225-7.

Conference Publications

  • Masek, M., Kelly, L., Snell, J., Wheat, D., Lam, P. (2023). Interactive Evolutionary Computation for Strategy Discovery in Multi-Phase Operations. GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (743-746). Association for Computing Machinery. https://doi.org/10.1145/3583133.3590644.

2022

Conference Publications

  • Kelly, L., Masek, M., Lam, P. (2022). Environment driven dynamic decomposition for cooperative coevolution of multi-agent systems. GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference (1218–1226). Association for Computing Machinery. https://doi.org/10.1145/3512290.3528759.

2021

Conference Publications

  • Masek, M., Lam, P., Rybicki, T., Snell, J., Wheat, D., Kelly, L., Glassborow, D., Smith-Gander, C. (2021). The Open Maritime Traffic Analysis Dataset. MODSIM2021, 24th International Congress on Modelling and Simulation (981-987). Modelling and Simulation Society of Australia and New Zealand. https://doi.org/10.36334/modsim.2021.M9.masek.
  • Masek, M., Lam, P., Kelly, L., Benke, L., Papasimeon, M. (2021). A Genetic Programming Framework for Novel Behaviour Discovery in Air Combat Scenarios. Data and Decision Sciences in Action 2. Proceedings of the ASOR/DORS Conference 2018 (263-277). Springer. https://doi.org/10.1007/978-3-030-60135-5_19.

2019

Journal Articles

  • Lam, P., Masek, M., Kelly, L., Papasimeon, M., Benke, L. (2019). A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics. Operations Research Perspectives, 6(2019), Article Number 100123. https://doi.org/10.1016/j.orp.2019.100123.

Conference Publications

  • Masek, M., Lam, P., Kelly, L., Wong, M. (2019). Evolving behaviour trees for automated discovery of novel combat strategy in real-time strategy wargames. MODSIM2019, 23rd International Congress on Modelling and Simulation (277-283). Modelling and Simulation Society of Australia and New Zealand. https://ro.ecu.edu.au/ecuworkspost2013/7571.

2018

Conference Publications

  • Masek, M., Lam, P., Benke, L., Kelly, L., Papasimeon, M. (2018). Discovering Emergent Agent Behaviour with Evolutionary Finite State Machines. PRIMA 2018: Principles and Practice of Multi-Agent Systems (19-34). Springer. https://doi.org/10.1007/978-3-030-03098-8_2.

Research Projects

  • Evolutionary Algorithms for Real Time Strategy games, Defence Science and Technology Group of the Department of Defence, Grant, 2018 ‑ 2019, $53,373.
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