Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR. - Richard Dazeley; Byeong Ho Kang 001 (Australian Conference on Artificial Intelligence, 2003)

Rated MCRDR: Finding non-Linear Relationships Between Classifications in MCRDR. - Richard Dazeley; Byeong Ho Kang 001 (HIS, 2003)

An Augmentation Hybrid System for Document Classification and Rating. - Richard Dazeley; Byeong Ho Kang 001 (PRICAI, 2004)

An Approach for Generalising Symbolic Knowledge. - Richard Dazeley; Byeong Ho Kang 001 (Australasian Conference on Artificial Intelligence, 2008)

Detecting the Knowledge Boundary with Prudence Analysis. - Richard Dazeley; Byeong Ho Kang 001 (Australasian Conference on Artificial Intelligence, 2008)

On the Limitations of Scalarisation for Multi-objective Reinforcement Learning of Pareto Fronts. - Peter Vamplew 001; John Yearwood; Richard Dazeley; Adam Berry (Australasian Conference on Artificial Intelligence, 2008)

Generalising Symbolic Knowledge in Online Classification and Prediction. - Richard Dazeley; Byeong Ho Kang 001 (PKAW, 2008)

Epistemological Approach to the Process of Practice. - Richard Dazeley; Byeong Ho Kang 001 (Minds Mach., 2008)

Constructing Stochastic Mixture Policies for Episodic Multiobjective Reinforcement Learning Tasks. - Peter Vamplew 001; Richard Dazeley; Ewan Barker; Andrei V. Kelarev (Australasian Conference on Artificial Intelligence, 2009)

Automatically determining phishing campaigns using the USCAP methodology. - Robert Layton; Paul A. Watters; Richard Dazeley (eCrime, 2010)

The Ballarat Incremental Knowledge Engine. - Richard Dazeley; Philip Warner; Scott Johnson; Peter Vamplew 001 (PKAW, 2010)

Consensus Clustering and Supervised Classification for Profiling Phishing Emails in Internet Commerce Security. - Richard Dazeley; John Yearwood; Byeong Ho Kang 001; Andrei V. Kelarev (PKAW, 2010)

Fault-tolerant data aggregation scheme for monitoring of critical events in grid based healthcare sensor networks. - Ather Saeed; Andrew Stranieri; Richard Dazeley (SpringSim (HPC), 2011)

Online knowledge validation with prudence analysis in a document management application. - Richard Dazeley; Sung Sik Park; Byeong Ho Kang 001 (Expert Syst. Appl., 2011)

How much material on BitTorrent is infringing content? A case study. - Paul Andrew Watters; Robert Layton; Richard Dazeley (Inf. Secur. Tech. Rep., 2011)

Empirical evaluation methods for multiobjective reinforcement learning algorithms. - Peter Vamplew 001; Richard Dazeley; Adam Berry; Rustam Issabekov; Evan Dekker (Mach. Learn., 2011)

Unsupervised authorship analysis of phishing webpages. - Robert Layton; Paul A. Watters; Richard Dazeley (ISCIT, 2012)

Detection of CAN by Ensemble Classifiers Based on Ripple Down Rules. - Andrei V. Kelarev; Richard Dazeley; Andrew Stranieri; John Yearwood; Herbert F. Jelinek (PKAW, 2012)

RM and RDM, a Preliminary Evaluation of Two Prudent RDR Techniques. - Omaru Maruatona; Peter Vamplew 001; Richard Dazeley (PKAW, 2012)

Recentred local profiles for authorship attribution. - Robert Layton; Paul Andrew Watters; Richard Dazeley (Nat. Lang. Eng., 2012)

Local n-grams for Author Identification Notebook for PAN at CLEF 2013. - Robert Layton; Paul A. Watters; Richard Dazeley (CLEF (Working Notes), 2013)

A Survey of Multi-Objective Sequential Decision-Making. - Diederik M. Roijers; Peter Vamplew 001; Shimon Whiteson; Richard Dazeley (J. Artif. Intell. Res., 2013)

Automated unsupervised authorship analysis using evidence accumulation clustering. - Robert Layton; Paul Andrew Watters; Richard Dazeley (Nat. Lang. Eng., 2013)

Evaluating authorship distance methods using the positive Silhouette coefficient. - Robert Layton; Paul Andrew Watters; Richard Dazeley (Nat. Lang. Eng., 2013)

Reinforcement Learning of Pareto-Optimal Multiobjective Policies Using Steering. - Peter Vamplew 001; Rustam Issabekov; Richard Dazeley; Cameron Foale (Australasian Conference on Artificial Intelligence, 2015)

Authorship analysis of aliases: Does topic influence accuracy? - Robert Layton; Paul A. Watters; Richard Dazeley (Nat. Lang. Eng., 2015)

Evaluating Accuracy in Prudence Analysis for Cyber Security. - Omaru Maruatona; Peter Vamplew 001; Richard Dazeley; Paul A. Watters (ICONIP (5), 2017)

Softmax exploration strategies for multiobjective reinforcement learning. - Peter Vamplew 001; Richard Dazeley; Cameron Foale (Neurocomputing, 2017)

Steering approaches to Pareto-optimal multiobjective reinforcement learning. - Peter Vamplew 001; Rustam Issabekov; Richard Dazeley; Cameron Foale; Adam Berry; Tim Moore; Douglas C. Creighton (Neurocomputing, 2017)

Rapid Anomaly Detection Using Integrated Prudence Analysis (IPA). - Omaru Maruatona; Peter Vamplew 001; Richard Dazeley; Paul A. Watters (PAKDD (Workshops), 2018)

Human-aligned artificial intelligence is a multiobjective problem. - Peter Vamplew 001; Richard Dazeley; Cameron Foale; Sally Firmin; Jane Mummery (Ethics Inf. Technol., 2018)

Non-functional regression: A new challenge for neural networks. - Peter Vamplew 001; Richard Dazeley; Cameron Foale; Tanveer A. Choudhury (Neurocomputing, 2018)

Memory-Based Explainable Reinforcement Learning. - Francisco Cruz 002; Richard Dazeley; Peter Vamplew 001 (Australasian Conference on Artificial Intelligence, 2019)

An Empirical Study of Reward Structures for Actor-Critic Reinforcement Learning in Air Combat Manoeuvring Simulation. - Budi Kurniawan; Peter Vamplew 001; Michael Papasimeon; Richard Dazeley; Cameron Foale (Australasian Conference on Artificial Intelligence, 2019)

A Robust Approach for Continuous Interactive Reinforcement Learning. - Cristian Millán-Arias; Bruno J. T. Fernandes; Francisco Cruz 002; Richard Dazeley; Sérgio Fernandes 001 (HAI, 2020)

A Comparison of Humanoid Robot Simulators: A Quantitative Approach. - Angel Ayala; Francisco Cruz 002; Diego Campos; Rodrigo Rubio; Bruno J. T. Fernandes; Richard Dazeley (ICDL-EPIROB, 2020)

A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions. - Peter Vamplew 001; Cameron Foale; Richard Dazeley (CoRR, 2020)

Discrete-to-Deep Supervised Policy Learning. - Budi Kurniawan; Peter Vamplew 001; Michael Papasimeon; Richard Dazeley; Cameron Foale (CoRR, 2020)

Explainable robotic systems: Interpreting outcome-focused actions in a reinforcement learning scenario. - Francisco Cruz 002; Richard Dazeley; Peter Vamplew 001 (CoRR, 2020)

Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment. - Ithan Moreira; Javier Rivas; Francisco Cruz 002; Richard Dazeley; Angel Ayala; Bruno J. T. Fernandes (CoRR, 2020)

A multi-objective deep reinforcement learning framework. - Thanh Thi Nguyen 001; Ngoc Duy Nguyen; Peter Vamplew 001; Saeid Nahavandi; Richard Dazeley; Chee Peng Lim (Eng. Appl. Artif. Intell., 2020)

Language Representations for Generalization in Reinforcement Learning. - Goodger Nikolaj; Peter Vamplew 001; Cameron Foale; Richard Dazeley (ACML, 2021)

A Robust Approach for Continuous Interactive Actor-Critic Algorithms. - Cristian Millán-Arias; Bruno J. T. Fernandes; Francisco Cruz 002; Richard Dazeley; Sérgio Fernandes 001 (IEEE Access, 2021)

Levels of explainable artificial intelligence for human-aligned conversational explanations. - Richard Dazeley; Peter Vamplew 001; Cameron Foale; Charlotte Young; Sunil Aryal; Francisco Cruz 002 (Artif. Intell., 2021)

Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task. - Angel Ayala; Francisco Cruz 002; Bruno J. T. Fernandes; Richard Dazeley (CoRR, 2021)

A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments. - Hung Son Nguyen; Francisco Cruz 002; Richard Dazeley (CoRR, 2021)

Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety. - Peter Vamplew 001; Cameron Foale; Richard Dazeley; Adam Bignold (Eng. Appl. Artif. Intell., 2021)

usfAD: a robust anomaly detector based on unsupervised stochastic forest. - Sunil Aryal; KC Santosh; Richard Dazeley (Int. J. Mach. Learn. Cybern., 2021)

A Prioritized objective actor-critic method for deep reinforcement learning. - Ngoc Duy Nguyen; Thanh Thi Nguyen 001; Peter Vamplew 001; Richard Dazeley; Saeid Nahavandi (Neural Comput. Appl., 2021)

Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios. - Francisco Cruz 002; Charlotte Young; Richard Dazeley; Peter Vamplew 001 (IROS, 2022)

A practical guide to multi-objective reinforcement learning and planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (Auton. Agents Multi Agent Syst., 2022)

Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). - Peter Vamplew 001; Benjamin J. Smith; Johan Källström; Gabriel de Oliveira Ramos; Roxana Radulescu; Diederik M. Roijers; Conor F. Hayes; Fredrik Heintz; Patrick Mannion; Pieter J. K. Libin; Richard Dazeley; Cameron Foale (Auton. Agents Multi Agent Syst., 2022)

Robust Pareto solutions for convex quadratic multiobjective optimization problems under data uncertainty. - Thai Doan Chuong; Vicky H. Mak-Hau; John Yearwood; Richard Dazeley; M. T. Nguyen; T. Cao (Ann. Oper. Res., 2022)

Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks. - Adrian Ly; Richard Dazeley; Peter Vamplew 001; Francisco Cruz 002; Sunil Aryal (CoRR, 2022)

Broad-persistent Advice for Interactive Reinforcement Learning Scenarios. - Francisco Cruz 002; Adam Bignold; Hung Son Nguyen; Richard Dazeley; Peter Vamplew 001 (CoRR, 2022)

Discrete-to-deep reinforcement learning methods. - Budi Kurniawan; Peter Vamplew 001; Michael Papasimeon; Richard Dazeley; Cameron Foale (Neural Comput. Appl., 2022)

The impact of environmental stochasticity on value-based multiobjective reinforcement learning. - Peter Vamplew 001; Cameron Foale; Richard Dazeley (Neural Comput. Appl., 2022)

Scalar Reward is Not Enough. - Peter Vamplew 001; Benjamin J. Smith; Johan Källström; Gabriel de Oliveira Ramos; Roxana Radulescu; Diederik M. Roijers; Conor F. Hayes; Friedrik Hentz; Patrick Mannion; Pieter J. K. Libin; Richard Dazeley; Cameron Foale (AAMAS, 2023)

A Brief Guide to Multi-Objective Reinforcement Learning and Planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (AAMAS, 2023)

A Wearable Multi-Sensor Fusion Approach for Gender Recognition based on Deep Learning. - Supriya Roy; Bahareh Nakisa; Pubudu N. Pathirana; Richard Dazeley (ICBRA, 2023)

Weighted Point Cloud Normal Estimation. - Weijia Wang; Xuequan Lu; Di Shao; Xiao Liu 004; Richard Dazeley; Antonio Robles-Kelly; Wei Pan 010 (ICME, 2023)

Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency. - Adrian Ly; Richard Dazeley; Peter Vamplew 001; Francisco Cruz 002; Sunil Aryal (IJCNN, 2023)

Current Status and Trends of Engineering Entrepreneurship Education in Australian Universities. - Jianhua Li 002; Sophie McKenzie; Richard Dazeley; Frank Jiang 001; Keshav Sood (CoRR, 2023)

A conceptual framework for externally-influenced agents: an assisted reinforcement learning review. - Adam Bignold; Francisco Cruz 002; Matthew E. Taylor; Tim Brys; Richard Dazeley; Peter Vamplew 001; Cameron Foale (J. Ambient Intell. Humaniz. Comput., 2023)

Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments. - Hung Son Nguyen; Francisco Cruz 002; Richard Dazeley (Sensors, 2023)

Overcoming weaknesses of density peak clustering using a data-dependent similarity measure. - Zafaryab Rasool; Sunil Aryal; Mohamed Reda Bouadjenek; Richard Dazeley (Pattern Recognit., 2023)

Human-aligned reinforcement learning for autonomous agents and robots. - Francisco Cruz 002; Thommen George Karimpanal; Miguel A. Solis; Pablo Barros 001; Richard Dazeley (Neural Comput. Appl., 2023)

Explainable reinforcement learning for broad-XAI: a conceptual framework and survey. - Richard Dazeley; Peter Vamplew 001; Francisco Cruz 002 (Neural Comput. Appl., 2023)

AI apology: interactive multi-objective reinforcement learning for human-aligned AI. - Hadassah Harland; Richard Dazeley; Bahareh Nakisa; Francisco Cruz 002; Peter Vamplew 001 (Neural Comput. Appl., 2023)

Human engagement providing evaluative and informative advice for interactive reinforcement learning. - Adam Bignold; Francisco Cruz 002; Richard Dazeley; Peter Vamplew 001; Cameron Foale (Neural Comput. Appl., 2023)

Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario. - Francisco Cruz 002; Richard Dazeley; Peter Vamplew 001; Ithan Moreira (Neural Comput. Appl., 2023)

Persistent rule-based interactive reinforcement learning. - Adam Bignold; Francisco Cruz 002; Richard Dazeley; Peter Vamplew 001; Cameron Foale (Neural Comput. Appl., 2023)

Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. - Peter Vamplew 001; Cameron Foale; Conor F. Hayes; Patrick Mannion; Enda Howley; Richard Dazeley; Scott Johnson; Johan Källström; Gabriel de Oliveira Ramos; Roxana Radulescu; Willem Röpke; Diederik M. Roijers (AAMAS, 2024)

TopFormer: Topology-Aware Transformer for Point Cloud Registration. - Sheldon Fung; Wei Pan 010; Xiao Liu 004; John Yearwood; Richard Dazeley; Xuequan Lu (CVM (1), 2024)

Margin-Bounded Confidence Scores for Out-of-Distribution Detection. - Lakpa Dorje Tamang; Mohamed Reda Bouadjenek; Richard Dazeley; Sunil Aryal (ICDM, 2024)

Value function interference and greedy action selection in value-based multi-objective reinforcement learning. - Peter Vamplew 001; Cameron Foale; Richard Dazeley (CoRR, 2024)

Data-driven Machinery Fault Detection: A Comprehensive Review. - Dhiraj Neupane; Mohamed Reda Bouadjenek; Richard Dazeley; Sunil Aryal (CoRR, 2024)

IReCa: Intrinsic Reward-enhanced Context-aware Reinforcement Learning for Human-AI Coordination. - Xin Hao; Bahareh Nakisa; Mohammad Naim Rastgoo; Richard Dazeley (CoRR, 2024)

Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment. - Peter Vamplew 001; Conor F. Hayes; Cameron Foale; Richard Dazeley; Hadassah Harland (CoRR, 2024)

Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI. - Hadassah Harland; Richard Dazeley; Peter Vamplew 001; Hashini Senaratne; Bahareh Nakisa; Francisco Cruz 002 (CoRR, 2024)

AI Apology: A Critical Review of Apology in AI Systems. - Hadassah Harland; Richard Dazeley; Hashini Senaratne; Peter Vamplew 001; Francisco Cruz 002; Bahareh Nakisa (CoRR, 2024)

Elastic step DQN: A novel multi-step algorithm to alleviate overestimation in Deep Q-Networks. - Adrian Ly; Richard Dazeley; Peter Vamplew 001; Francisco Cruz 002; Sunil Aryal (Neurocomputing, 2024)

Segmentation-driven feature-preserving mesh denoising. - Weijia Wang; Wei Pan 010; Chaofan Dai; Richard Dazeley; Lei Wei 002; Bernard Rolfe; Xuequan Lu (Vis. Comput., 2024)

DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion. - Yinghui Li; Qianyu Zhou 001; Jingyu Gong; Ye Zhu 002; Richard Dazeley; Xinkui Zhao; Xuequan Lu (AAAI, 2025)

Data-driven machinery fault diagnosis: A comprehensive review. - Dhiraj Neupane; Mohamed Reda Bouadjenek; Richard Dazeley; Sunil Aryal (Neurocomputing, 2025)

An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments. - Kewen Ding; Peter Vamplew 001; Cameron Foale; Richard Dazeley (Knowl. Eng. Rev., 2025)

Masked Autoencoders in 3D Point Cloud Representation Learning. - Jincen Jiang; Xuequan Lu; Lizhi Zhao; Richard Dazeley; Meili Wang (IEEE Trans. Multim., 2025)

Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges. - Hao Xu; Arbind Agrahari Baniya; Sam Well; Mohamed Reda Bouadjenek; Richard Dazeley; Sunil Aryal (CoRR, 2025)

Ensemble Elastic DQN: A novel multi-step ensemble approach to address overestimation in deep value-based reinforcement learning. - Adrian Ly; Richard Dazeley; Peter Vamplew 001; Francisco Cruz 002; Sunil Aryal (CoRR, 2025)

Multi-Scale Attention and Gated Shifting for Fine-Grained Event Spotting in Videos. - Hao Xu; Arbind Agrahari Baniya; Sam Wells; Mohamed Reda Bouadjenek; Richard Dazeley; Sunil Aryal (CoRR, 2025)

Handling Out-of-Distribution Data: A Survey. - Lakpa Dorje Tamang; Mohamed Reda Bouadjenek; Richard Dazeley; Sunil Aryal (CoRR, 2025)

Improving out-of-distribution detection by enforcing confidence margin. - Lakpa Dorje Tamang; Mohamed Reda Bouadjenek; Richard Dazeley; Sunil Aryal (Knowl. Inf. Syst., 2025)