Peter Vamplew

John Yearwood, Peter Vamplew, Md. Shamsul Huda, 2011, 2011 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

Correction to: Griefers Versus the Griefed - What Motivates Them to Play Massively Multiplayer Online Role-Playing Games?

Charlynn Miller, Peter Vamplew, Leigh Achterbosch, 2017, The Computer Games Journal.

On the Limitations of Scalarisation for Multi-objective Reinforcement Learning of Pareto Fronts

John Yearwood, Peter Vamplew, Richard Dazeley, 2008, Australasian Conference on Artificial Intelligence.

Caliko v1.1.2

Philip Smith, Peter Vamplew, Cameron Foale, 2016 .

Proceedings of the Eighth Australasian Data Mining Conference - Volume 101

Andrew Stranieri, Kok-Leong Ong, Peter Christen, 2008, AusDM 2008.

Griefing in MMORPGs

Peter Vamplew, Leigh Achterbosch, 2019, Encyclopedia of Computer Graphics and Games.

The Combative Accretion Model - Multiobjective Optimisation Without Explicit Pareto Ranking

Peter Vamplew, Adam Berry, P. Vamplew, 2005, EMO.

Rapid Anomaly Detection Using Integrated Prudence Analysis (IPA)

Paul A. Watters, Peter Vamplew, Richard Dazeley, 2018, PAKDD.

A heuristic gene regulatory networks model for cardiac function and pathology

Andrew Stranieri, Herbert F. Jelinek, Peter Vamplew, 2016, 2016 Computing in Cardiology Conference (CinC).

API Based Discrimination of Ransomware and Benign Cryptographic Programs

Iqbal Gondal, Joarder Kamruzzaman, Paul A. Watters, 2020, ICONIP.

Applying Clustering and Ensemble Clustering Approaches to phishing Profiling

John Yearwood, Andrei V. Kelarev, Bahadorreza Ofoghi, 2009, AusDM.

Memory-Based Explainable Reinforcement Learning

Peter Vamplew, Richard Dazeley, Francisco Cruz, 2019, Australasian Conference on Artificial Intelligence.

On-Line Reinforcement Learning Using Cascade Constructive Neural Networks

Peter Vamplew, Robert Ollington, R. Ollington, 2005, KES.

Griefers versus the Griefed — what motivates them to play Massively Multiplayer Online Role-Playing Games?

Charlynn Miller, Peter Vamplew, Leigh Achterbosch, 2017, The Computer Games Journal.

Integrating Biological Heuristics and Gene Expression Data for GeneRegulatory Network Inference

Andrew Stranieri, Herbert F. Jelinek, Peter Vamplew, 2017 .

Accelerating Real-Valued Genetic Algorithms Using Mutation-with-Momentum

Peter Vamplew, Adam Berry, Luke Temby, 2005, Australian Conference on Artificial Intelligence.

Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario

Peter Vamplew, Richard Dazeley, Francisco Cruz, 2020, Neural Computing and Applications.

Caliko: An Inverse Kinematics Software Library Implementation of the FABRIK Algorithm

Peter Vamplew, Cameron Foale, Alastair Lansley, 2016 .

Concurrent Q‐learning: Reinforcement learning for dynamic goals and environments

Peter Vamplew, Robert Ollington, R. Ollington, 2005, Int. J. Intell. Syst..

Explainable robotic systems: Interpreting outcome-focused actions in a reinforcement learning scenario pdf

Peter Vamplew, Richard Dazeley, Francisco Cruz, 2020, ArXiv.

Weblogs for market research: finding more relevant opinion documents using system fusion

John Yearwood, Peter Vamplew, Deanna J. Osman, 2009, Online Inf. Rev..

Ganking, corpse camping and ninja looting from the perception of the MMORPG community: acceptable behavior or unacceptable griefing?

Charlynn Miller, Peter Vamplew, Leigh Achterbosch, 2013, IE '13.

Constructing Stochastic Mixture Policies for Episodic Multiobjective Reinforcement Learning Tasks

Andrei V. Kelarev, Peter Vamplew, Richard Dazeley, 2009, Australasian Conference on Artificial Intelligence.

Visualising the value of water

Peter Vamplew, Bradley Mitchell, Kevin O'Toole, 2012 .

Special issue on multi-objective reinforcement learning

Marco Wiering, Madhu Chetty, Peter Vamplew, 2017, Neurocomputing.

Motivational Factors of Australian Mobile Gamers

Peter Vamplew, Grant Meredith, Leigh Achterbosch, 2020, ACSW.

Unsupervised Segmentation of Industrial Images Using Markov Random Field Model

John Yearwood, Peter Vamplew, Mofakharul Islam, 2008, EIAT/IETA.

Unsupervised Color Textured Image Segmentation Using Cluster Ensembles and MRF Model

John Yearwood, Peter Vamplew, Mofakharul Islam, 2007, SCSS.

Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning pdf

Peter Vamplew, Richard Dazeley, Cameron Foale, 2020, ArXiv.

A Survey of Multi-Objective Sequential Decision-Making pdf

Shimon Whiteson, Diederik M. Roijers, Peter Vamplew, 2013, J. Artif. Intell. Res..

Using Stereotypes to Improve Early-Match Poker Play

Peter Vamplew, Robert Layton, Chris Turville, 2008, Australasian Conference on Artificial Intelligence.

SoniFight: Software to Provide Additional Sonification Cues to Video Games for Visually Impaired Players

Philip Smith, Peter Vamplew, Cameron Foale, 2018, The Computer Games Journal.

Recognition of sign language using neural networks

Peter Vamplew, P. Vamplew, 1996 .

Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction

Chee Peng Lim, Andrew Stranieri, Saeid Nahavandi, 2014, Neural Computing and Applications.

Levels of explainable artificial intelligence for human-aligned conversational explanations

Peter Vamplew, Richard Dazeley, Cameron Foale, 2021, Artif. Intell..

Language Representations for Generalization in Reinforcement Learning

Peter Vamplew, Richard Dazeley, Cameron Foale, 2021, ACML.

Explainable reinforcement learning for broad-XAI: a conceptual framework and survey pdf

Peter Vamplew, Richard Dazeley, Francisco Cruz, 2021, Neural Computing and Applications.

A practical guide to multi-objective reinforcement learning and planning pdf

Fredrik Heintz, Marcello Restelli, Timothy Verstraeten, 2021, Autonomous Agents and Multi-Agent Systems.

Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario

Peter Vamplew, Richard Dazeley, Francisco Cruz, 2020, Neural Computing and Applications.

The impact of environmental stochasticity on value-based multiobjective reinforcement learning

Peter Vamplew, Richard Dazeley, Cameron Foale, 2021, Neural Computing and Applications.

A Prioritized objective actor-critic method for deep reinforcement learning

Saeid Nahavandi, Peter Vamplew, Richard Dazeley, 2021, Neural Computing and Applications.

A conceptual framework for externally-influenced agents: an assisted reinforcement learning review pdf

Matthew E. Taylor, Tim Brys, Peter Vamplew, 2020, Journal of Ambient Intelligence and Humanized Computing.

Discrete-to-deep reinforcement learning methods

Peter Vamplew, Richard Dazeley, Cameron Foale, 2021, Neural Computing and Applications.

Persistent Rule-based Interactive Reinforcement Learning pdf

Peter Vamplew, Richard Dazeley, Cameron Foale, 2021, Neural Computing and Applications.

Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021) pdf

Fredrik Heintz, Diederik M. Roijers, Peter Vamplew, 2021, Autonomous Agents and Multi-Agent Systems.

Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety

Peter Vamplew, Richard Dazeley, Cameron Foale, 2021, Eng. Appl. Artif. Intell..

An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users

Peter Vamplew, Richard Dazeley, Cameron Foale, 2021, Biomimetics.

DETECTING K-COMPLEXES FOR SLEEP STAGE IDENTIFICATION USING NONSMOOTH OPTIMIZATION

Peter Vamplew, Gleb Beliakov, Julien Ugon, 2011, The ANZIAM Journal.

A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions pdf

Peter Vamplew, Richard Dazeley, Cameron Foale, 2020, ArXiv.