Publications

Why people skip music? On predicting music skips using deep reinforcement learning

Meggetto Francesco, Revie Crawford, Levine John, Moshfeghi Yashar

Proceedings of the 2023 Conference on Human Information Interaction and Retrieval 8th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2023, pp. 95-106 (2023)

https://doi.org/10.1145/3576840.3578312

Why people skip music? On predicting music skips using deep reinforcement learning

Meggetto Francesco, Revie Crawford, Levine John, Moshfeghi Yashar

(2023)

https://doi.org/10.48550/arXiv.2301.03881

On skipping behaviour types in music streaming sessions

Meggetto Francesco, Revie Crawford, Levine John, Moshfeghi Yashar

CIKM '21 International Conference on Information and Knowledge Management, Proceedings, pp. 3333-3337 (2021)

https://doi.org/10.1145/3459637.3482123

A continuous information gain measure to find the most discriminatory problems for AI benchmarking

Stephenson Matthew, Anderson Damien, Khalifa Ahmed, Levine John, Renz Jochen, Togelius Julian, Salge Christoph

2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings 2020 IEEE Congress on Evolutionary Computation, CEC 2020 (2020)

https://doi.org/10.1109/CEC48606.2020.9185834

Ensemble Decision Systems for general video game playing

Anderson Damien, Rodgers Philip, Levine John, Guerrero-Romero Cristina, Perez-Liebana Diego

IEEE Conference on Games (COG) 2019 (2019)

Open-world games : an exploration of creative play

Arnott Lorna, Levine John, Shanks Gillian

Early Learning in the Digital Age (2019) (2019)

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Professional activities

IEEE Congress on Evolutionary Computation

Member of programme committee

2011

EPSRC (Engineering and Physical Sciences Research Council) (External organisation)

Member

2010

International Journal of Metaheuristics (Journal)

Editorial board member

1/2008

EPSRC (Engineering and Physical Sciences Research Council) (External organisation)

Member

2008

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Projects

Capita PhD Studentship Application Awarded: Open World Gaming for Young Children's Creative Play

Arnott, Lorna (Academic) Levine, John (Academic)

01-Jan-2015 - 01-Jan-2019

EVOLVING AND GENERALISING VERY HIGH QUALITY KNOWLEDGE FOR AI PLANNING

Levine, John (Principal Investigator) Fox, Maria (Co-investigator) Long, Derek (Co-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.

01-Jan-2006 - 31-Jan-2010

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Address

Computer and Information Sciences
Livingstone Tower

Livingstone Tower

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