A survey of multi-objective sequential decision-making DM Roijers, P Vamplew, S Whiteson, R Dazeley Journal of Artificial Intelligence Research 48, 67-113, 2013 | 724 | 2013 |
Empirical evaluation methods for multiobjective reinforcement learning algorithms P Vamplew, R Dazeley, A Berry, R Issabekov, E Dekker Machine learning 84, 51-80, 2011 | 360 | 2011 |
A practical guide to multi-objective reinforcement learning and planning CF Hayes, R Rădulescu, E Bargiacchi, J Källström, M Macfarlane, ... Autonomous Agents and Multi-Agent Systems 36 (1), 26, 2022 | 258 | 2022 |
Authorship attribution for twitter in 140 characters or less R Layton, P Watters, R Dazeley 2010 Second Cybercrime and Trustworthy Computing Workshop, 1-8, 2010 | 204 | 2010 |
On the limitations of scalarisation for multi-objective reinforcement learning of pareto fronts P Vamplew, J Yearwood, R Dazeley, A Berry AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint …, 2008 | 153 | 2008 |
Human-aligned artificial intelligence is a multiobjective problem P Vamplew, R Dazeley, C Foale, S Firmin, J Mummery Ethics and Information Technology 20, 27-40, 2018 | 143 | 2018 |
A multi-objective deep reinforcement learning framework TT Nguyen, ND Nguyen, P Vamplew, S Nahavandi, R Dazeley, CP Lim Engineering Applications of Artificial Intelligence 96, 103915, 2020 | 126 | 2020 |
Levels of explainable artificial intelligence for human-aligned conversational explanations R Dazeley, P Vamplew, C Foale, C Young, S Aryal, F Cruz Artificial Intelligence 299, 103525, 2021 | 106 | 2021 |
Consensus clustering and supervised classification for profiling phishing emails in internet commerce security R Dazeley, JL Yearwood, BH Kang, AV Kelarev Knowledge Management and Acquisition for Smart Systems and Services: 11th …, 2010 | 72 | 2010 |
Automated unsupervised authorship analysis using evidence accumulation clustering R Layton, P Watters, R Dazeley Natural Language Engineering 19 (1), 95-120, 2013 | 69 | 2013 |
Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks P Vamplew, R Dazeley, E Barker, A Kelarev AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint …, 2009 | 60 | 2009 |
Scalar reward is not enough: A response to silver, singh, precup and sutton (2021) P Vamplew, BJ Smith, J Källström, G Ramos, R Rădulescu, DM Roijers, ... Autonomous Agents and Multi-Agent Systems 36 (2), 41, 2022 | 59 | 2022 |
Softmax exploration strategies for multiobjective reinforcement learning P Vamplew, R Dazeley, C Foale Neurocomputing 263, 74-86, 2017 | 56 | 2017 |
Automatically determining phishing campaigns using the uscap methodology R Layton, P Watters, R Dazeley 2010 eCrime Researchers Summit, 1-8, 2010 | 54* | 2010 |
Explainable reinforcement learning for broad-xai: a conceptual framework and survey R Dazeley, P Vamplew, F Cruz Neural Computing and Applications 35 (23), 16893-16916, 2023 | 50 | 2023 |
Recentred local profiles for authorship attribution R Layton, P Watters, R Dazeley Natural Language Engineering 18 (3), 293-312, 2012 | 47 | 2012 |
Deep reinforcement learning with interactive feedback in a human–robot environment I Moreira, J Rivas, F Cruz, R Dazeley, A Ayala, B Fernandes Applied Sciences 10 (16), 5574, 2020 | 40 | 2020 |
Memory-based explainable reinforcement learning F Cruz, R Dazeley, P Vamplew AI 2019: Advances in Artificial Intelligence: 32nd Australasian Joint …, 2019 | 40 | 2019 |
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario F Cruz, R Dazeley, P Vamplew, I Moreira Neural Computing and Applications 35 (25), 18113-18130, 2023 | 38 | 2023 |
A comparison of humanoid robot simulators: A quantitative approach A Ayala, F Cruz, D Campos, R Rubio, B Fernandes, R Dazeley 2020 Joint IEEE 10th international conference on development and learning …, 2020 | 37 | 2020 |