Richard Dazeley
Richard Dazeley
Associate Professor, School of Information Technology, Deakin University
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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 (1), 51-80, 2011
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
Australasian joint conference on artificial intelligence, 372-378, 2008
Human-aligned artificial intelligence is a multiobjective problem
P Vamplew, R Dazeley, C Foale, S Firmin, J Mummery
Ethics and Information Technology 20 (1), 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
Automated unsupervised authorship analysis using evidence accumulation clustering
R Layton, P Watters, R Dazeley
Natural Language Engineering 19 (1), 95-120, 2013
Consensus clustering and supervised classification for profiling phishing emails in internet commerce security
R Dazeley, JL Yearwood, BH Kang, AV Kelarev
Pacific Rim Knowledge Acquisition Workshop, 235-246, 2010
Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks
P Vamplew, R Dazeley, E Barker, A Kelarev
Australasian joint conference on artificial intelligence, 340-349, 2009
Automatically determining phishing campaigns using the uscap methodology
R Layton, P Watters, R Dazeley
2010 eCrime Researchers Summit, 1-8, 2010
Recentred local profiles for authorship attribution
R Layton, P Watters, R Dazeley
Natural Language Engineering 18 (3), 293-312, 2012
Softmax exploration strategies for multiobjective reinforcement learning
P Vamplew, R Dazeley, C Foale
Neurocomputing 263, 74-86, 2017
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), 1-59, 2022
Steering approaches to Pareto-optimal multiobjective reinforcement learning
P Vamplew, R Issabekov, R Dazeley, C Foale, A Berry, T Moore, ...
Neurocomputing 263, 26-38, 2017
Memory-based explainable reinforcement learning
F Cruz, R Dazeley, P Vamplew
Australasian Joint Conference on Artificial Intelligence, 66-77, 2019
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
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
Evaluating authorship distance methods using the positive Silhouette coefficient
R Layton, P Watters, R Dazeley
Natural Language Engineering 19 (4), 517-535, 2013
Unsupervised authorship analysis of phishing webpages
R Layton, P Watters, R Dazeley
2012 International Symposium on Communications and Information Technologies …, 2012
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario
F Cruz, R Dazeley, P Vamplew, I Moreira
Neural Computing and Applications, 1-18, 2021
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