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Dr John Levine

Senior Lecturer

Computer and Information Sciences


Open-world games : an exploration of creative play
Arnott Lorna, Levine John, Shanks Gillian
Early Learning in the Digital AgeEarly Learning in the Digital Age, (2018)
Deceptive games
Anderson Damien, Stephenson Matthew, Togelius Julian, Salge Christian, Levine John, Renz Jochen
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol 10784, pp. 376-391, (2018)
A hybrid integer programming and variable neighborhood search algorithm to solve Nurse Rostering Problems
Rahimian Erfan, Akartunali Kerem, Levine John
European Journal of Operational Research Vol 258, pp. 411–423, (2017)
A hybrid integer and constraint programming approach to solve nurse rostering problems
Rahimian Erfan, Akartunali Kerem, Levine John
Computers & Operations Research Vol 82, pp. 83-94, (2017)
AI-based game design patterns
Treanor Mike, Zook Alexander, Eladhari Mirjam P, Togelius Julian, Smith Gillian, Cook Michael, Thompson Tommy, Magerko Brian, Levine John, Smith Adam
Proceedings of the 10th International Conference on the Foundations of Digital Games 2015 (FDG 2015), (2015)
A hybrid constraint integer programming approach to solve nurse scheduling problems
Rahimian Erfan, Akartunali Kerem, Levine John
Mista 2015 Proceedings of the 7th Multidisciplinary International Scheduling ConferenceProceedings of the Multidisciplinary International Conference on Scheduling: Theory and Applications, pp. 429-442, (2015)

more publications

Professional activities

IEEE Congress on Evolutionary Computation
Member of programme committee
EPSRC (Engineering and Physical Sciences Research Council) (External organisation)
International Journal of Metaheuristics (Journal)
Editorial board member
EPSRC (Engineering and Physical Sciences Research Council) (External organisation)

more professional activities


Levine, John (Principal Investigator)
This project is concerned with making AI planning practical by exploiting evolutionary learning techniques to acquire control knowledge automatically. This control knowledge can be used to prune useless branches of the search space and to propose promising branches. Whilst human-coded control rules have been shown to be useful in planning, their specification is an effort-intensive process which makes them difficult or impossible to generalise. Their use has tended to be confined to relatively simple domain models about which the human has a good understanding of the dynamics. We propose to learn powerful rules automatically from both static and dynamic sources of information about the planning domain and the process of planning within that domain. Furthermore, we will develop a method for learning generic rules that apply to classes of domains and that can be automatically customised for a particular domain. This reduces or even removes the burden on the human and results in scalable planning technology.
Period 01-Jun-2006 - 31-Mar-2010

more projects


Computer and Information Sciences
Livingstone Tower

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