Objects
Goodger, Nikolaj, Vamplew, Peter, Foale, Cameron, Dazeley, Richard. Ubiquity Press; 2023. A nethack learning environment language wrapper for autonomous agents.
Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun. IEEE; 2019. Evolved similarity techniques in malware analysis.
Wang, Jin, Lim, Cheepeng, Creighton, Douglas, Khorsavi, Abbas, Nahavandi, Saeid, Ugon, Julien, Vamplew, Peter, Stranieri, Andrew, Martin, Laura, Freischmidt, Anton. Springer-Verlag London; 2015. Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction.
Moloney, David, Sukhorukova, Nadezda, Vamplew, Peter, Ugon, Julien, Li, Gang, Beliakov, Gleb, Philippe, Carole, Amiel, Hélène, Ugon, Adrien. Detecting K-complexes for sleep stage identification using nonsmooth optimization. 2012.
Kelarev, Andrei, Yearwood, John, Vamplew, Peter, Abawajy, Jemal, Chowdhury, Morshed. ; 2011. Optimization and matrix constructions for classification of data.
Mirzanejad, Mohammad, Ebrahimi, Morteza, Vamplew, Peter, Veisi, Hadi. Cambridge University Press; 2022. An online scalarization multi-objective reinforcement learning algorithm : TOPSIS Q-learning.
Layton, Robert, Vamplew, Peter, Turville, Christopher. Auckland Springer; 2008. Using stereotypes to improve early-match poker play.
Maruatona, Omaru, Vamplew, Peter, Dazeley, Richard, Watters, Paul. Cham: Springer International Publishing; 2018. Rapid anomaly detection using integrated prudence analysis (IPA).
Nankervis, Scott, Meredith, Grant, Vamplew, Peter, Fotinatos, Nina. Massey University; 2012. Taming the devil: a game based approach to teaching immunology.
Islam, Mofakharul, Yearwood, John, Vamplew, Peter. Springer Netherlands; 2008. Unsupervised color textured image segmentation using cluster ensembles and MRF mdel.
Kelarev, Andrei, Yearwood, John, Vamplew, Peter. A polynomial ring construction for the classification of data. 2009.
Vamplew, Peter, Dazeley, Richard, Barker, Ewan, Kelarev, Andrei. Heidelberg, Germany Springer; 2009. Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks.
Dazeley, Richard, Vamplew, Peter, Foale, Cameron, Young, Cameron, Aryal, Sunil, Cruz, Francisco. Elsevier B.V.; 2021. Levels of explainable artificial intelligence for human-aligned conversational explanations.
Black, Paul, Sohail, Ammar, Gondal, Iqbal, Kamruzzaman, Joarder, Vamplew, Peter, Watters, Paul. Springer Science and Business Media Deutschland GmbH; 2020. API based discrimination of ransomware and benign cryptographic programs.
Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun. Springer; 2021. Reanimating historic malware samples.
Webb, Dean, Yearwood, John, Vamplew, Peter, Ma, Liping, Ofoghi, Bahadorreza, Kelarev, Andrei. University of Melbourne, Melbourne, Victoria :; 2009. Applying clustering and ensemble clustering approaches to phishing profiling.
Vamplew, Peter, Dazeley, Richard, Foale, Cameron. Elsevier Ltd; 2017. Softmax exploration strategies for multiobjective reinforcement learning.
Block, Jessica, Graymore, Michelle, Wallis, Anne, Vamplew, Peter, Mitchell, Bradley, O'Toole, Kevin, McRae-Williams, Pamela. VURRN Press; 2012. Visualising the value of water.
Achterbosch, Leigh, Miller, Charlynn, Turville, Christopher, Vamplew, Peter. Computer Games Journal Ltd.; 2014. Griefers versus the Griefed - what motivates them to play Massively Multiplayer Online Role-Playing Games?.
Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun. Institute of Electrical and Electronics Engineers Inc.; 2020. Identifying cross-version function similarity using contextual features.
Zarnegar, Armita, Vamplew, Peter, Stranieri, Andrew. Wellington, New Zealand : Conferences in Research and Practice in Information Technology - CRPIT; 2009. Inference of gene expression networks using memetic gene expression programming.
Osman, Deanna, Yearwood, John, Vamplew, Peter. ; 2010. Automated opinion detection : Implications of the level of agreement between human raters.
Ul Haq, Ikram, Gondal, Iqbal, Vamplew, Peter, Brown, Simon. Springer Verlag; 2019. Categorical features transformation with compact one-hot encoder for fraud detection in distributed environment.
Kurniawan, Budi, Vamplew, Peter, Papasimeon, Michael, Dazeley, Richard, Foale, Cameron. Springer Science and Business Media Deutschland GmbH; 2022. Discrete-to-deep reinforcement learning methods.
Vamplew, Peter, Issabekov, Rustam, Dazeley, Richard, Foale, Cameron. Springer Verlag; 2015. Reinforcement learning of pareto-optimal multiobjective policies using steering.
Cruz, Francisco, Young, Charlotte, Dazeley, Richard, Vamplew, Peter. Institute of Electrical and Electronics Engineers Inc.; 2022. Evaluating human-like explanations for robot actions in reinforcement learning scenarios.
Vamplew, Peter, Berry, Adam. Seattle, USA : Association for Computing Machinery (ACM); 2006. An efficient approach to unbounded bi-objective archives : Introducing the Mak_Tree algorithm.
Mukherjee, Subhasis, Yearwood, John, Vamplew, Peter, Huda, Shamsul. Sydney, NSW IEEE; 2011. Reinforcement learning approach to AIBO robot's decision making process in Robosoccer's goal keeper problem.
Ollington, Robert, Vamplew, Peter, Swanson, John. Heidelberg Springer Berlin; 2009. Incorporating expert advice into reinforcement learning using constructive neural networks.
Sukhorukova, Nadezda, Stranieri, Andrew, Ofoghi, Bahadorreza, Vamplew, Peter, Saleem, Muhammad Saad, Ma, Liping, Ugon, Adrien, Ugon, Julien, Muecke, Nial, Amiel, Hélène, Philippe, Carole, Bani-Mustafa, Ahmed, Huda, Shamsul, Bertoli, Marcello, Levy, P, Ganascia, J.G. Brisbane Australian Computer Society; 2010. Automatic sleep stage identification: difficulties and possible solutions.
Osman, Deanna, Yearwood, John, Vamplew, Peter. Emerald; 2009. Weblogs for market research : Finding more relevant opinion documents using system fusion.
Islam, Mofakharul, Yearwood, John, Vamplew, Peter. Online Conference Springer; 2009. Unsupervised segmentation of Industrial Images using Markov Random Field Model.
Nguyen, Thanh, Nguyen, Ngoc, Vamplew, Peter, Nahavandi, Saeid, Dazeley, Richard, Lim, Chee. Elsevier Ltd; 2020. A multi-objective deep reinforcement learning framework.
Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron. MDPI AG; 2021. An evaluation methodology for interactive reinforcement learning with simulated users.
Osman, Deanna, Yearwood, John, Vamplew, Peter. Gold Coast, Queensland, Victoria : Australian Computer Society; 2007. Using corpus analysis to inform research into opinion detection in blogs.
Achterbosch, Leigh, Miller, Charlynn, Vamplew, Peter. ACM Digital Library; 2013. Ganking, corpse camping and ninja looting from the perception of the MMORPG community: Acceptable behaviour or unacceptable griefing?.
Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron. Springer Science and Business Media Deutschland GmbH; 2023. Human engagement providing evaluative and informative advice for interactive reinforcement learning.
Johnson, David, Malhotra, Vishy, Vamplew, Peter. Webology (University of Tehran); 2006. More effective web search using bigrams and trigrams.
Harland, Hadassah, Dazeley, Richard, Nakisa, Bahareh, Cruz, Francisco, Vamplew, Peter. Springer Science and Business Media Deutschland GmbH; 2023. AI apology : interactive multi-objective reinforcement learning for human-aligned AI.
Islam, Mofakharul, Vamplew, Peter, Yearwood, John. Online conference; 2009. MRF model based unsupervised color textured image segmentation using multidimensional spatially variant finite mixture model.
Osman, Deanna, Yearwood, John, Vamplew, Peter. Melbourne, Victoria : IEEE; 2008. Weblogs for market research : Improving opinion detection using system fusion.
Vamplew, Peter, Dazeley, Richard, Foale, Cameron, Firmin, Sally, Mummery, Jane. Springer Netherlands; 2018. Human-aligned artificial intelligence is a multiobjective problem.
Greenwood, Jordan, Achterbosch, Leigh, Meredith, Grant, Vamplew, Peter. Assoc Computing Machinery; 2020. Motivational factors of Australian mobile gamers.
Achterbosch, Leigh, Miller, Charlynn, Vamplew, Peter. Taylor and Francis Ltd.; 2017. A taxonomy of griefer type by motivation in massively multiplayer online role-playing games.
Vamplew, Peter, Dazeley, Richard, Berry, Adam, Issabekov, Rustam, Dekker, Evan. Empirical evaluation methods for multiobjective reinforcement learning algorithms. 2011.
Dazeley, Richard, Warner, Philip, Johnson, Scott, Vamplew, Peter. Daegu SpringerLink; 2010. The ballarat incremental knowledge engine.
Torney, Rosemary, Vamplew, Peter, Yearwood, John. ; 2012. Using psycholinguistic features for profiling first language of authors.
Dazeley, Richard, Vamplew, Peter, Cruz, Francisco. Springer Science and Business Media Deutschland GmbH; 2023. Explainable reinforcement learning for broad-XAI: a conceptual framework and survey.
Maruatona, Omaru, Vamplew, Peter, Dazeley, Richard. Ballarat, VIC IEEE Computer Society Conference Publishing Services; 2012. Prudent fraud detection in internet banking.
Nguyen, Ngoc, Nguyen, Thanh, Vamplew, Peter, Dazeley, Richard, Nahavandi, Saeid. Springer Science and Business Media Deutschland GmbH; 2021. A prioritized objective actor-critic method for deep reinforcement learning.
Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun. MDPI AG; 2020. Function similarity using family context.
Roijers, Diederik, Vamplew, Peter, Whiteson, Shimon, Dazeley, Richard. A survey of multi-objective sequential decision-making. 2013.
Goodger, Nikolaj, Vamplew, Peter, Foale, Cameron, Dazeley, Richard. ACML; 2021. Language representations for generalization in reinforcement learning.
Zarnegar, Armita, Jelinek, Herbert, Vamplew, Peter, Stranieri, Andrew. Assoc Computing Machinery; 2019. Integrating biological heuristics and gene expression data for gene regulatory network inference.
Vamplew, Peter, Stranieri, Andrew, Ong, Kok-Leong, Christen, Peter, Kennedy, Paul. Sydney, Australia Australian Computer Society; 2011. Data Mining and Analytics 2011: Proceedings of the Ninth Australasian Data Mining Conference.
Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Moreira, Ithan. Springer Science and Business Media Deutschland GmbH; 2023. Explainable robotic systems : understanding goal-driven actions in a reinforcement learning scenario.
Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar. MDPI AG; 2020. Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine.
Torney, Rosemary, Yearwood, John, Vamplew, Peter, Kelarev, Andrei. Hershey IGI Global; 2012. Applications of machine learning for linguistic analysis of texts.
Vamplew, Peter, Dermoudy, Julien. Sydney, NSW Australian Computer Society; 2005. An anti-plagiarism editor for software development courses.
Vamplew, Peter, Dazeley, Richard, Foale, Cameron, Choudhury, Tanveer. Elsevier B.V.; 2018. Non-functional regression : A new challenge for neural networks.
Bignold, Adam, Cruz, Francisco, Taylor, Matthew, Brys, Tim, Dazeley, Richard, Vamplew, Peter, Foale, Cameron. Springer Science and Business Media Deutschland GmbH; 2023. A conceptual framework for externally-influenced agents: an assisted reinforcement learning review.
Zarnegar, Armita, Vamplew, Peter, Stranieri, Andrew, Jelinek, Herbert. IEEE; 2016. A heuristic gene regulatory networks model for cardiac function and pathology.
Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar. MDPI AG; 2019. A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks.
Berry, Adam, Vamplew, Peter. Guanajuato, Mexico : Springer; 2005. The Combative Accretion Model-; Multiobjective Optimisation Without Explicit Pareto Ranking.
Vamplew, Peter, Yearwood, John, Dazeley, Richard, Berry, Adam. Auckland, New Zealand : Springer; 2008. On the limitations of scalarisation for multi-objective reinforcement learning of Pareto fronts.
Issabekov, Rustam, Vamplew, Peter. Sydney, NSW Springer Berlin Heidelberg; 2012. An empirical comparison of two common multiobjective reinforcement learning algorithms.
Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter. Springer Verlag; 2018. An anomaly intrusion detection system using C5 decision tree classifier.
Khraisat, Ansam, Iqbal, Gondal, Vamplew, Peter, Kamruzzaman, Joarder. Springer Nature; 2019. Survey of intrusion detection systems : techniques, datasets and challenges.
Vamplew, Peter, Foale, Cameron, Dazeley, Richard, Bignold, Adam. Elsevier Ltd; 2021. Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety.
Vamplew, Peter, Ollington, Robert, Hepburn, Mark. Hobart, Australia : Springer; 2006. Enhanced temporal difference learning using compiled eligibility traces.
Maruatona, Omaru, Vamplew, Peter, Dazeley, Richard. Springer; 2012. RM and RDM, a preliminary evaluation of two prudent RDR Techniques.
Vamplew, Peter, Issabekov, Rustam, Dazeley, Richard, Foale, Cameron, Berry, Adam, Moore, Tim, Creighton, Douglas. Elsevier; 2017. Steering approaches to Pareto-optimal multiobjective reinforcement learning.
Vamplew, Peter, Foale, Cameron, Dazeley, Richard. Springer Science and Business Media Deutschland GmbH; 2022. The impact of environmental stochasticity on value-based multiobjective reinforcement learning.
Foale, Cameron, Vamplew, Peter. RMIT University, Melbourne, Victoria : School of Creative Media, RMIT University; 2007. Portal-based sound propagation for first-person computer games.
Vamplew, Peter, Smith, Benjamin, Källström, Johan, Ramos, Gabriel, Rădulescu, Roxana, Roijers, Diederik, Hayes, Conor, Heintz, Fredrik, Mannion, Patrick, Libin, Pieter, Dazeley, Richard, Foale, Cameron. Springer; 2022. Scalar reward is not enough : a response to Silver, Singh, Precup and Sutton (2021).
Vamplew, Peter, Smith, Benjamin, Källström, Johan, Ramos, Gabriel, Rădulescu, Roxana, Roijers, Diederik, Hayes, Conor, Heintz, Fredrik, Mannion, Patrick, Libin, Pieter, Dazeley, Richard, Foale, Cameron. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS); 2023. Scalar reward is not enough JAAMAS Track.
Dazeley, Richard, Vamplew, Peter, Bignold, Adam. ; 2015. Coarse Q-Learning : Addressing the convergence problem when quantizing continuous state variables.
Ly, Adrian, Dazeley, Richard, Vamplew, Peter, Cruz, Francisco, Aryal, Sunil. Institute of Electrical and Electronics Engineers Inc.; 2023. Elastic step DDPG : multi-step reinforcement learning for improved sample efficiency.
Ly, Adrian, Dazeley, Richard, Vamplew, Peter, Cruz, Francisco, Aryal, Sunil. Elsevier B.V.; 2024. Elastic step DQN : a novel multi-step algorithm to alleviate overestimation in deep Q-networks.
Vamplew, Peter, Foale, Cameron, Hayes, Conor, Mannion, Patrick, Howley, Enda, Dazeley, Richard, Johnson, Scott, Källström, Johan, Ramos, Gabriel, Rădulescu, Roxana, Röpke, Willem, Roijers, Diederik. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS); 2024. Utility-based reinforcement learning : unifying single-objective and multi-objective reinforcement learning.
Mitelut, Catalin, Smith, Ben, Vamplew, Peter. ML Research Press; 2024. Position : intent-aligned ai systems must optimize for agency preservation.
Hayes, Conor, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa, Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai, Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS); 2023. A brief guide to multi-objective reinforcement learning and planning JAAMAS track.
Kurniawan, Budi, Vamplew, Peter, Papasimeon, Michael, Dazeley, Richard, Foale, Cameron. AAMAS; 2020. Discrete-to-deep supervised policy learning an effective training method for neural reinforcement learning.
Hayes, Conor, Rădulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa, Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai, Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik. Springer; 2022. A practical guide to multi-objective reinforcement learning and planning.