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ORCIDPeter Vamplew 0001
Person information
- affiliation: Federation University, Ballarat, VIC, Australia
- affiliation (2005 - 2014): University of Ballarat, VIC, Australia
Other persons with the same name
- Peter Vamplew 0002 — AMEC Offshore Services, Aberdeen, UK
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2020 – today
- 2025
[i22]Jayden Teoh Jing Teoh, Pradeep Varakantham, Peter Vamplew:
On Generalization Across Environments In Multi-Objective Reinforcement Learning. CoRR abs/2503.00799 (2025)- 2024
- [j35]Leigh Achterbosch
, Peter Vamplew, Evita March:
Assessing the impact of griefing in MMORPGs using self-determination theory. Comput. Hum. Behav. 161: 108388 (2024) - [j34]Adrian Ly, Richard Dazeley, Peter Vamplew, Francisco Cruz, Sunil Aryal:
Elastic step DQN: A novel multi-step algorithm to alleviate overestimation in Deep Q-Networks. Neurocomputing 576: 127170 (2024) - [c47]Peter Vamplew, 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:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. AAMAS 2024: 2717-2721 - [c46]Catalin Mitelut, Benjamin J. Smith, Peter Vamplew:
Position: Intent-aligned AI Systems Must Optimize for Agency Preservation. ICML 2024 - [i21]Kewen Ding, Peter Vamplew, Cameron Foale, Richard Dazeley:
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments. CoRR abs/2401.03163 (2024) - [i20]Peter Vamplew, 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:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. CoRR abs/2402.02665 (2024) - [i19]Peter Vamplew, Cameron Foale, Richard Dazeley:
Value function interference and greedy action selection in value-based multi-objective reinforcement learning. CoRR abs/2402.06266 (2024) - [i18]Peter Vamplew, Conor F. Hayes, Cameron Foale, Richard Dazeley, Hadassah Harland:
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment. CoRR abs/2410.11221 (2024) - [i17]Hadassah Harland, Richard Dazeley, Peter Vamplew, Hashini Senaratne, Bahareh Nakisa, Francisco Cruz:
Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI. CoRR abs/2410.23630 (2024) - [i16]Hadassah Harland, Richard Dazeley, Hashini Senaratne, Peter Vamplew, Francisco Cruz, Bahareh Nakisa:
AI Apology: A Critical Review of Apology in AI Systems. CoRR abs/2412.15787 (2024) - 2023
- [j33]Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale:
A conceptual framework for externally-influenced agents: an assisted reinforcement learning review. J. Ambient Intell. Humaniz. Comput. 14(4): 3621-3644 (2023) - [j32]Richard Dazeley, Peter Vamplew, Francisco Cruz:
Explainable reinforcement learning for broad-XAI: a conceptual framework and survey. Neural Comput. Appl. 35(23): 16893-16916 (2023) - [j31]Hadassah Harland, Richard Dazeley, Bahareh Nakisa, Francisco Cruz, Peter Vamplew:
AI apology: interactive multi-objective reinforcement learning for human-aligned AI. Neural Comput. Appl. 35(23): 16917-16930 (2023) - [j30]Francisco Cruz, Richard Dazeley, Peter Vamplew, Ithan Moreira:
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario. Neural Comput. Appl. 35(25): 18113-18130 (2023) - [j29]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Human engagement providing evaluative and informative advice for interactive reinforcement learning. Neural Comput. Appl. 35(25): 18215-18230 (2023) - [j28]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Persistent rule-based interactive reinforcement learning. Neural Comput. Appl. 35(32): 23411-23428 (2023) - [c45]Peter Vamplew, 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:
Scalar Reward is Not Enough. AAMAS 2023: 839-841 - [c44]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, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - [c43]Adrian Ly, Richard Dazeley, Peter Vamplew, Francisco Cruz, Sunil Aryal:
Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency. IJCNN 2023: 1-6 - [i15]Catalin Mitelut, Ben Smith, Peter Vamplew:
Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety. CoRR abs/2305.19223 (2023) - 2022
- [j27]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, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j26]Peter Vamplew, 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:
Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). Auton. Agents Multi Agent Syst. 36(2): 41 (2022) - [j25]Mohammad Mirzanejad, Morteza Ebrahimi, Peter Vamplew, Hadi Veisi:
An online scalarization multi-objective reinforcement learning algorithm: TOPSIS Q-learning. Knowl. Eng. Rev. 37: e7 (2022) - [j24]Budi Kurniawan, Peter Vamplew, Michael Papasimeon, Richard Dazeley, Cameron Foale:
Discrete-to-deep reinforcement learning methods. Neural Comput. Appl. 34(3): 1713-1733 (2022) - [j23]Peter Vamplew, Cameron Foale, Richard Dazeley:
The impact of environmental stochasticity on value-based multiobjective reinforcement learning. Neural Comput. Appl. 34(3): 1783-1799 (2022) - [c42]Francisco Cruz, Charlotte Young, Richard Dazeley, Peter Vamplew:
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios. IROS 2022: 894-901 - [i14]Francisco Cruz, Charlotte Young, Richard Dazeley, Peter Vamplew:
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios. CoRR abs/2207.03214 (2022) - [i13]Adrian Ly, Richard Dazeley, Peter Vamplew, Francisco Cruz, Sunil Aryal:
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks. CoRR abs/2210.03325 (2022) - [i12]Francisco Cruz, Adam Bignold, Hung Son Nguyen, Richard Dazeley, Peter Vamplew:
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios. CoRR abs/2210.05187 (2022) - 2021
- [j22]Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal, Francisco Cruz:
Levels of explainable artificial intelligence for human-aligned conversational explanations. Artif. Intell. 299: 103525 (2021) - [j21]Peter Vamplew, Cameron Foale, Richard Dazeley, Adam Bignold:
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety. Eng. Appl. Artif. Intell. 100: 104186 (2021) - [j20]Ngoc Duy Nguyen, Thanh Thi Nguyen, Peter Vamplew, Richard Dazeley, Saeid Nahavandi:
A Prioritized objective actor-critic method for deep reinforcement learning. Neural Comput. Appl. 33(16): 10335-10349 (2021) - [c41]Goodger Nikolaj, Peter Vamplew, Cameron Foale, Richard Dazeley:
Language Representations for Generalization in Reinforcement Learning. ACML 2021: 390-405 - [i11]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Persistent Rule-based Interactive Reinforcement Learning. CoRR abs/2102.02441 (2021) - [i10]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, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i9]Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal, Francisco Cruz:
Levels of explainable artificial intelligence for human-aligned conversational explanations. CoRR abs/2107.03178 (2021) - [i8]Richard Dazeley, Peter Vamplew, Francisco Cruz:
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey. CoRR abs/2108.09003 (2021) - [i7]Peter Vamplew, 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:
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021). CoRR abs/2112.15422 (2021) - 2020
- [j19]Thanh Thi Nguyen, Ngoc Duy Nguyen, Peter Vamplew, Saeid Nahavandi, Richard Dazeley, Chee Peng Lim:
A multi-objective deep reinforcement learning framework. Eng. Appl. Artif. Intell. 96: 103915 (2020) - [c40]Jordan Greenwood, Leigh Achterbosch, Grant Meredith, Peter Vamplew:
Motivational Factors of Australian Mobile Gamers. ACSW 2020: 45:1-45:6 - [c39]Paul Black, Ammar Sohail, Iqbal Gondal, Joarder Kamruzzaman, Peter Vamplew, Paul A. Watters:
API Based Discrimination of Ransomware and Benign Cryptographic Programs. ICONIP (2) 2020: 177-188 - [c38]Ikram Ul Haq, Iqbal Gondal, Peter Vamplew:
Unified Expression Ripple Down Rules based Fraud Detection Technique for Scalable Data. ICDM 2020: 1-16 - [c37]Paul Black, Iqbal Gondal, Peter Vamplew, Arun Lakhotia:
Identifying Cross-Version Function Similarity Using Contextual Features. TrustCom 2020: 810-818 - [i6]Peter Vamplew, Cameron Foale, Richard Dazeley:
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions. CoRR abs/2004.06277 (2020) - [i5]Budi Kurniawan, Peter Vamplew, Michael Papasimeon, Richard Dazeley, Cameron Foale:
Discrete-to-Deep Supervised Policy Learning. CoRR abs/2005.02057 (2020) - [i4]Francisco Cruz, Richard Dazeley, Peter Vamplew:
Explainable robotic systems: Interpreting outcome-focused actions in a reinforcement learning scenario. CoRR abs/2006.13615 (2020) - [i3]Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale:
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review. CoRR abs/2007.01544 (2020) - [i2]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning. CoRR abs/2009.09575 (2020)
2010 – 2019
- 2019
- [j18]Ansam Khraisat, Iqbal Gondal, Peter Vamplew, Joarder Kamruzzaman:
Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecur. 2(1): 20 (2019) - [c36]Armita Zarnegar, Herbert F. Jelinek, Peter Vamplew, Andrew Stranieri:
Integrating Biological Heuristics and Gene Expression Data for Gene Regulatory Network Inference. ACSW 2019: 37:1-37:10 - [c35]Budi Kurniawan, Peter Vamplew, Michael Papasimeon, Richard Dazeley, Cameron Foale:
An Empirical Study of Reward Structures for Actor-Critic Reinforcement Learning in Air Combat Manoeuvring Simulation. Australasian Conference on Artificial Intelligence 2019: 54-65 - [c34]Francisco Cruz, Richard Dazeley, Peter Vamplew:
Memory-Based Explainable Reinforcement Learning. Australasian Conference on Artificial Intelligence 2019: 66-77 - [c33]Ikram Ul Haq, Iqbal Gondal, Peter Vamplew:
Enhancing Model Performance for Fraud Detection by Feature Engineering and Compact Unified Expressions. ICA3PP (2) 2019: 399-409 - [c32]Paul Black, Iqbal Gondal, Peter Vamplew, Arun Lakhotia:
Evolved Similarity Techniques in Malware Analysis. TrustCom/BigDataSE 2019: 404-410 - [r1]Leigh Achterbosch, Peter Vamplew:
Griefing in MMORPGs. Encyclopedia of Computer Graphics and Games 2019 - 2018
- [j17]Leigh Achterbosch, Charlynn Miller, Christopher Turville, Peter Vamplew:
Correction to: Griefers Versus the Griefed - What Motivates Them to Play Massively Multiplayer Online Role-Playing Games? Comput. Games J. 7(1): 43 (2018) - [j16]Alastair Lansley, Peter Vamplew, Cameron Foale, Philip Smith:
SoniFight: Software to Provide Additional Sonification Cues to Video Games for Visually Impaired Players. Comput. Games J. 7(2): 115-130 (2018) - [j15]Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin, Jane Mummery:
Human-aligned artificial intelligence is a multiobjective problem. Ethics Inf. Technol. 20(1): 27-40 (2018) - [j14]Peter Vamplew, Richard Dazeley, Cameron Foale, Tanveer A. Choudhury:
Non-functional regression: A new challenge for neural networks. Neurocomputing 314: 326-335 (2018) - [c31]Ikram Ul Haq, Iqbal Gondal, Peter Vamplew, Simon Brown:
Categorical Features Transformation with Compact One-Hot Encoder for Fraud Detection in Distributed Environment. AusDM 2018: 69-80 - [c30]Omaru Maruatona, Peter Vamplew, Richard Dazeley, Paul A. Watters:
Rapid Anomaly Detection Using Integrated Prudence Analysis (IPA). PAKDD (Workshops) 2018: 137-141 - [c29]Ansam Khraisat, Iqbal Gondal, Peter Vamplew:
An Anomaly Intrusion Detection System Using C5 Decision Tree Classifier. PAKDD (Workshops) 2018: 149-155 - 2017
- [j13]Leigh Achterbosch, Charlynn Miller, Peter Vamplew:
A taxonomy of griefer type by motivation in massively multiplayer online role-playing games. Behav. Inf. Technol. 36(8): 846-860 (2017) - [j12]Madalina M. Drugan, Marco A. Wiering, Peter Vamplew, Madhu Chetty:
Special issue on multi-objective reinforcement learning. Neurocomputing 263: 1-2 (2017) - [j11]Peter Vamplew, Rustam Issabekov, Richard Dazeley, Cameron Foale, Adam Berry, Tim Moore, Douglas C. Creighton:
Steering approaches to Pareto-optimal multiobjective reinforcement learning. Neurocomputing 263: 26-38 (2017) - [j10]Peter Vamplew, Richard Dazeley, Cameron Foale:
Softmax exploration strategies for multiobjective reinforcement learning. Neurocomputing 263: 74-86 (2017) - [c28]Vishakha Sharma, Andrew Stranieri, Julien Ugon, Peter Vamplew, Laura Martin:
An Agile Group Aware Process beyond CRISP-DM: A Hospital Data Mining Case Study. ICCDA 2017: 109-113 - [c27]Omaru Maruatona, Peter Vamplew, Richard Dazeley, Paul A. Watters:
Evaluating Accuracy in Prudence Analysis for Cyber Security. ICONIP (5) 2017: 407-417 - 2016
- [c26]Armita Zarnegar, Peter Vamplew, Andrew Stranieri, Herbert F. Jelinek:
A Heuristic Gene Regulatory Networks Model for Cardiac Function and Pathology. CinC 2016 - 2015
- [j9]Jin Wang, Chee Peng Lim, Douglas C. Creighton, Abbas Khosravi, Saeid Nahavandi, Julien Ugon, Peter Vamplew, Andrew Stranieri, Laura Martin, Anton Freischmidt:
Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction. Neural Comput. Appl. 26(2): 277-289 (2015) - [c25]Peter Vamplew, Rustam Issabekov, Richard Dazeley, Cameron Foale:
Reinforcement Learning of Pareto-Optimal Multiobjective Policies Using Steering. Australasian Conference on Artificial Intelligence 2015: 596-608 - 2014
- [j8]Leigh Achterbosch, Charlynn Miller, Christopher Turville, Peter Vamplew:
Griefers versus the Griefed - what motivates them to play Massively Multiplayer Online Role-Playing Games? Comput. Games J. 3(1): 5-18 (2014) - [i1]Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson, Richard Dazeley:
A Survey of Multi-Objective Sequential Decision-Making. CoRR abs/1402.0590 (2014) - 2013
- [j7]Diederik M. Roijers, Peter Vamplew, Shimon Whiteson, Richard Dazeley:
A Survey of Multi-Objective Sequential Decision-Making. J. Artif. Intell. Res. 48: 67-113 (2013) - [c24]Leigh Achterbosch, Charlynn Miller, Peter Vamplew:
Ganking, corpse camping and ninja looting from the perception of the MMORPG community: acceptable behavior or unacceptable griefing? IE 2013: 19:1-19:8 - 2012
- [j6]Rosemary Torney, Peter Vamplew, John Yearwood:
Using psycholinguistic features for profiling first language of authors. J. Assoc. Inf. Sci. Technol. 63(6): 1256-1269 (2012) - [c23]Rustam Issabekov, Peter Vamplew:
An Empirical Comparison of Two Common Multiobjective Reinforcement Learning Algorithms. Australasian Conference on Artificial Intelligence 2012: 626-636 - [c22]Omaru Maruatona, Peter Vamplew, Richard Dazeley:
RM and RDM, a Preliminary Evaluation of Two Prudent RDR Techniques. PKAW 2012: 188-194 - 2011
- [j5]Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov, Evan Dekker:
Empirical evaluation methods for multiobjective reinforcement learning algorithms. Mach. Learn. 84(1-2): 51-80 (2011) - [c21]Subhasis Mukherjee, John Yearwood, Peter Vamplew, Md. Shamsul Huda:
Reinforcement Learning Approach to AIBO Robot's Decision Making Process in Robosoccer's Goal Keeper Problem. SNPD 2011: 24-30 - [e1]Peter Vamplew, Andrew Stranieri, Kok-Leong Ong, Peter Christen, Paul J. Kennedy:
Ninth Australasian Data Mining Conference, AusDM 2011, Ballarat, Australia, December 2011. CRPIT 121, Australian Computer Society 2011, ISBN 978-1-921770-02-9 [contents] - 2010
- [j4]Deanna J. Osman, John Yearwood, Peter Vamplew:
Automated opinion detection: Implications of the level of agreement between human raters. Inf. Process. Manag. 46(3): 331-342 (2010) - [c20]Richard Dazeley, Philip Warner, Scott Johnson, Peter Vamplew:
The Ballarat Incremental Knowledge Engine. PKAW 2010: 195-207
2000 – 2009
- 2009
- [j3]Deanna J. Osman, John Yearwood, Peter Vamplew:
Weblogs for market research: finding more relevant opinion documents using system fusion. Online Inf. Rev. 33(5): 873-888 (2009) - [c19]Armita Zarnegar, Peter Vamplew, Andrew Stranieri:
Inference of Gene Expression Networks Using Memetic Gene Expression Programming. ACSC 2009: 17-23 - [c18]Peter Vamplew, Richard Dazeley, Ewan Barker, Andrei V. Kelarev:
Constructing Stochastic Mixture Policies for Episodic Multiobjective Reinforcement Learning Tasks. Australasian Conference on Artificial Intelligence 2009: 340-349 - [c17]John Yearwood, Dean Webb, Liping Ma, Peter Vamplew, Bahadorreza Ofoghi, Andrei V. Kelarev:
Applying Clustering and Ensemble Clustering Approaches to phishing Profiling. AusDM 2009: 25-34 - [p1]Robert Ollington, Peter Vamplew, John Swanson:
Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks. Constructive Neural Networks 2009: 207-224 - 2008
- [c16]Peter Vamplew, John Yearwood, Richard Dazeley, Adam Berry:
On the Limitations of Scalarisation for Multi-objective Reinforcement Learning of Pareto Fronts. Australasian Conference on Artificial Intelligence 2008: 372-378 - [c15]Robert Layton, Peter Vamplew, Chris Turville:
Using Stereotypes to Improve Early-Match Poker Play. Australasian Conference on Artificial Intelligence 2008: 584-593 - [c14]Mofakharul Islam, John Yearwood, Peter Vamplew:
Unsupervised Segmentation of Industrial Images Using Markov Random Field Model. EIAT/IETA 2008: 369-374 - [c13]Mofakharul Islam, Peter Vamplew, John Yearwood:
MRF Model Based Unsupervised Color Textured Image Segmentation Using Multidimensional Spatially Variant Finite Mixture Model. EIAT/IETA 2008: 375-380 - 2007
- [c12]Deanna J. Osman, John Yearwood, Peter Vamplew:
Using Corpus Analysis to Inform Research into Opinion Detection in Blogs. AusDM 2007: 65-75 - [c11]Mofakharul Islam, John Yearwood, Peter Vamplew:
Unsupervised Color Textured Image Segmentation Using Cluster Ensembles and MRF Model. SCSS (1) 2007: 323-328 - [c10]Cameron Foale, Peter Vamplew:
Portal-based sound propagation for first-person computer games. IE 2007: 9 - 2006
- [j2]David Johnson, Vishv M. Malhotra, Peter Vamplew:
More Effective Web Search Using Bigrams and Trigrams. Webology 3(4) (2006) - [c9]Peter Vamplew, Robert Ollington, Mark Hepburn:
Enhanced Temporal Difference Learning Using Compiled Eligibility Traces. Australian Conference on Artificial Intelligence 2006: 141-150 - [c8]Adam Berry, Peter Vamplew:
An efficient approach to unbounded bi-objective archives -: introducing the mak_tree algorithm. GECCO 2006: 619-626 - 2005
- [j1]Robert Ollington, Peter Vamplew:
Concurrent Q-learning: Reinforcement learning for dynamic goals and environments. Int. J. Intell. Syst. 20(10): 1037-1052 (2005) - [c7]Peter Vamplew, Julian R. Dermoudy:
An Anti-Plagiarism Editor for Software Development Courses. ACE 2005: 83-90 - [c6]Peter Vamplew, Robert Ollington:
Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning. Australian Conference on Artificial Intelligence 2005: 113-122 - [c5]Luke Temby, Peter Vamplew, Adam Berry:
Accelerating Real-Valued Genetic Algorithms Using Mutation-with-Momentum. Australian Conference on Artificial Intelligence 2005: 1108-1111 - [c4]Adam Berry, Peter Vamplew:
The Combative Accretion Model - Multiobjective Optimisation Without Explicit Pareto Ranking. EMO 2005: 77-91 - [c3]Peter Vamplew, Robert Ollington:
On-Line Reinforcement Learning Using Cascade Constructive Neural Networks. KES (3) 2005: 562-568 - 2003
- [c2]Adam Berry, Peter Vamplew:
A simplified artificial life model for multiobjective optimisation: a preliminary report. IEEE Congress on Evolutionary Computation 2003: 1331-1339
1990 – 1999
- 1995
- [c1]Peter Vamplew, Anthony Adams:
Recognition and anticipation of hand motions using a recurrent neural network. ICNN 1995: 2904-2907
Coauthor Index
[j34] [c47] [i21] [i20] [i19] [i18] [i17] [i16] [j33] [j32] [j31] [j30] [j29] [j28] [c45] [c44] [c43] [j27] [j26] [j24] [j23] [c42] [i14] [i13] [i12] [j22] [j21] [j20] [c41] [i11] [i10] [i9] [i8] [i7] [j19] [i6] [i5] [i4] [i3] [i2] [c35] [c34] [j15] [j14] [c30] [j11] [j10] [c27] [c25] [i1] [j7] [c22] [j5] [c20] [c18] [c16]
Diederik M. Roijers
aka: Diederik Marijn Roijers
aka: Diederik Marijn Roijers
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