Richard Dazeley
Richard Dazeley
Professor of Artificial Intelligence and Machine Learning, School of Information Technology, Deakin
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Cited by
Cited by
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
Empirical evaluation methods for multiobjective reinforcement learning algorithms
P Vamplew, R Dazeley, A Berry, R Issabekov, E Dekker
Machine learning 84, 51-80, 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
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
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
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
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
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
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
Automated unsupervised authorship analysis using evidence accumulation clustering
R Layton, P Watters, R Dazeley
Natural Language Engineering 19 (1), 95-120, 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
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
Softmax exploration strategies for multiobjective reinforcement learning
P Vamplew, R Dazeley, C Foale
Neurocomputing 263, 74-86, 2017
Automatically determining phishing campaigns using the uscap methodology
R Layton, P Watters, R Dazeley
2010 eCrime Researchers Summit, 1-8, 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
Recentred local profiles for authorship attribution
R Layton, P Watters, R Dazeley
Natural Language Engineering 18 (3), 293-312, 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
Memory-based explainable reinforcement learning
F Cruz, R Dazeley, P Vamplew
AI 2019: Advances in Artificial Intelligence: 32nd Australasian Joint …, 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
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
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