Professor Peter Vamplew
Research Adviser
Professor Peter Vamplew’s information technology expertise focuses on artificial intelligence, particularly reinforcement learning. Prof Vamplew is currently researching variations on reinforcement learning algorithms for multi-objective problems, which contribute to the explainability and safety of autonomous AI systems. Peter’s research has been published widely in highly-ranked international journals.
Peter co-leads the Australian Responsible Autonomous Agents Collective, a multi-institution research group which focuses on reinforcement learning and related topics. He is also a senior member of the Future of Life Institute’s Existential AI Risk research community.
Peter has been a Professor in Information Technology at Federation University Australia since 2023, and was previously an Associate Professor at Federation (2014-2023), and Senior Lecturer at the University of Ballarat (2005-2014). Prior to that he was a lecturer within the computing discipline at the University of Tasmania from 1991–2005, where he received his PhD in 1996.
Assessing the impact of griefing in MMORPGs using self-determination theory
Elastic step DQN: A novel multi-step algorithm to alleviate overestimation in Deep Q-Networks
Position: Intent-aligned AI Systems Must Optimize for Agency Preservation
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
A Brief Guide to Multi-Objective Reinforcement Learning and Planning JAAMAS track
A conceptual framework for externally-influenced agents: an assisted reinforcement learning review
AI apology: interactive multi-objective reinforcement learning for human-aligned AI
A NetHack Learning Environment Language Wrapper for Autonomous Agents
Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency
Explainable reinforcement learning for broad-XAI: a conceptual framework and survey
Human engagement providing evaluative and informative advice for interactive reinforcement learning
Persistent rule-based interactive reinforcement learning
Scalar Reward is Not Enough JAAMAS Track
A Low-Level Hybrid Intrusion Detection System Based on Hardware Performance Counters
An online scalarization multi-objective reinforcement learning algorithm: TOPSIS Q-learning
A practical guide to multi-objective reinforcement learning and planning
Discrete-to-deep reinforcement learning methods
Neural networks are effective function approximators, but hard to train in the reinforcement...
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios
Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021)
An evaluation methodology for interactive reinforcement learning with simulated users
Interactive reinforcement learning methods utilise an external information source to evaluate...
A Prioritized objective actor-critic method for deep reinforcement learning
An increasing number of complex problems have naturally posed significant challenges in...
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario
Language Representations for Generalization in Reinforcement Learning
Levels of explainable artificial intelligence for human-aligned conversational explanations
Over the last few years there has been rapid research growth into eXplainable Artificial...
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety
The concept of impact-minimisation has previously been proposed as an approach to addressing the...
Reanimating Historic Malware Samples
The impact of environmental stochasticity on value-based multiobjective reinforcement learning
A common approach to address multiobjective problems using reinforcement learning methods is to...
A multi-objective deep reinforcement learning framework
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL)...
API Based Discrimination of Ransomware and Benign Cryptographic Programs
Ransomware is a widespread class of malware that encrypts files in a victim’s computer and...
Discrete-to-Deep Supervised Policy Learning An effective training method for neural reinforcement learning
Enhancing Model Performance for Fraud Detection by Feature Engineering and Compact Unified Expressions
The performance of machine learning models can be improved in a variety of ways including...
Function Similarity Using Family Context
Finding changed and similar functions between a pair of binaries is an important problem in...
Griefing in MMORPGs
Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine
Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion...
Identifying cross-version function similarity using contextual features
The identification of similar functions in malware assists analysis by supporting the exclusion...
Motivational Factors of Australian Mobile Gamers
Mobile games are a fast growing industry, overtaking all other video game platforms with year on...
Reanimating historic malware samples
An Empirical Study of Reward Structures for Actor-Critic Reinforcement Learning in Air Combat Manoeuvring Simulation
Reinforcement learning techniques for solving complex problems are resource-intensive and take a...
A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks
The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on...
Categorical features transformation with compact one-hot encoder for fraud detection in distributed environment
Fraud detection for online banking is an important research area, but one of the challenges is...
Evolved similarity techniques in Malware Analysis
Malware authors are known to reuse existing code, this development process results in software...
Integrating Biological Heuristics and Gene Expression Data for Gene Regulatory Network Inference
Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and...
Memory-Based Explainable Reinforcement Learning
Reinforcement learning (RL) is a learning approach based on behavioral psychology used by...
Survey of intrusion detection systems:techniques, datasets and challenges
Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in...
An anomaly intrusion detection system using C5 decision tree classifier
Due to increase in intrusion activities over internet, many intrusion detection systems are...
Human-aligned artificial intelligence is a multiobjective problem
As the capabilities of artificial intelligence (AI) systems improve, it becomes important to...
Non-functional regression: A new challenge for neural networks
This work identifies an important, previously unaddressed issue for regression based on neural...
Participant observation of griefing in a journey through the World of Warcraft
Through the ethnographic method of participant observation in World of Warcraft, this paper aims...
Rapid anomaly detection using integrated prudence analysis (IPA)
Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based...
SoniFight: Software to Provide Additional Sonification Cues to Video Games for Visually Impaired Players
SoniFight is utility software designed to provide additional sonification cues to video games,...
An agile group aware process beyond CRISP-DM: A hospital data mining case study
The CRISP-DM methodology is commonly used in data analytics exercises within an organisation to...
A taxonomy of griefer type by motivation in massively multiplayer online role-playing games
There is an anti-social phenomenon known as griefing that occurs in online games. Griefing refers...
Evaluating accuracy in prudence analysis for cyber security
Conventional Knowledge-Based Systems (KBS) have no way of detecting or signalling when their...
Softmax exploration strategies for multiobjective reinforcement learning
Despite growing interest over recent years in applying reinforcement learning to multiobjective...
Special issue on multi-objective reinforcement learning
Steering approaches to Pareto-optimal multiobjective reinforcement learning
For reinforcement learning tasks with multiple objectives, it may be advantageous to learn...
A Heuristic Gene Regulatory Networks Model for Cardiac Function and Pathology
Genome-wide association studies (GWAS) and next-generation sequencing (NGS) has led to an...
Caliko: An Inverse Kinematics Software Library Implementation of the FABRIK Algorithm
The Caliko library is an implementation of the FABRIK (Forward And Backward Reaching Inverse...
Generating Synthetic Datasets for Experimental Validation of Fraud Detection
Frauds are dramatically increasing every year, resulting in billions of dollars in losses around...
Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction
A useful patient admission prediction model that helps the emergency department of a hospital...
Reinforcement learning of pareto-optimal multiobjective policies using steering
Griefers versus the Griefed - what motivates them to play Massively Multiplayer Online Role-Playing Games?
‘Griefing’ is a term used to describe when a player within a multiplayer online environment...
A Survey of Multi-Objective Sequential Decision-Making
Sequential decision-making problems with multiple objectives arise naturally in practice and pose...
Ganking, corpse camping and ninja looting from the perception of the MMORPG community: Acceptable behavior or unacceptable griefing?
Prudent fraud detection in internet banking
An empirical comparison of two common multiobjective reinforcement learning algorithms
In this paper we provide empirical data of the performance of the two most commonly used...
Applications of machine learning for linguistic analysis of texts
DETECTING K-COMPLEXES FOR SLEEP STAGE IDENTIFICATION USING NONSMOOTH OPTIMIZATION
The process of sleep stage identification is a labour-intensive task that involves the...
Optimization and matrix constructions for classification of data
Max-plus algebras and more general semirings have many useful applications and have been actively...
RM and RDM, a Preliminary Evaluation of Two Prudent RDR Techniques
Taming the Devil: A game based approach to teaching immunology
Using psycholinguistic features for profiling first language of authors
This study empirically evaluates the effectiveness of different feature types for the...
Visualising the value of water
Empirical evaluation methods for multiobjective reinforcement learning algorithms
While a number of algorithms for multiobjective reinforcement learning have been proposed, and a...
Reinforcement learning approach to AIBO robot's decision making process in Robosoccer's goal keeper problem
Automated Opinion Detection: Implications of the Level of Agreement Between Human Raters
The ability to agree with the TREC Blog06 opinion assessments was measured for seven human...
Automatic sleep stage identification: difficulties and possible solutions
The Ballarat Incremental Knowledge Engine
Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development...
WINDSCREEN: A climate change visualisation tool for water allocation decisions
A polynomial ring construction for the classification of data
Applying Clustering and Ensemble Clustering Approaches to Phishing Profiling
Constructing Stochastic Mixture Policies for Episodic Multiobjective Reinforcement Learning Tasks
Footy, flows and farms: a visualisation tool for determining community water allocation preferences
Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks
This paper presents and investigates a novel approach to using expert advice to speed up the...
Inference of Gene Expression Networks using Memetic Gene Expression Programming
MRF model based unsupervised color textured image segmentation using multidimensional spatially variant finite mixture model
We investigate and propose a novel approach to implement an unsupervised color image segmentation...
Unsupervised Segmentation of Industrial Images using Markov Random Field Model
We propose a novel approach to investigate and implement unsupervised image content understanding...
Weblogs for market research: Finding more relevant opinion documents using system fusion
On the limitations of scalarisation for multi-objective reinforcement learning of Pareto Fronts
Multiobjective reinforcement learning (MORL) extends RL to problems with multiple conflicting...
System fusion for opinion detection in weblogs
Unsupervised Color Textured Image Segmentation Using Cluster Ensembles and MRF Model
We propose a novel approach to implement robust unsupervised color image content understanding...
Using Stereotypes to Improve Early-Match Poker Play
Weblogs for market research: improving opinion detection using system fusion
Portal-based Sound Propagation for First-Person Computer Games
First-person computer games are a popular modern video game genre. A new method is proposed, the...
Using Corpus Analysis to Inform Research into Opinion Detection in Blogs
Opinion detection research relies on labeled docu-ments for training data, either by assumptions...
An efficient approach to unbounded bi-objective archives: Introducing the Mak_Tree algorithm
Given the prominence of elite archiving in contemporary multiobjective optimisation research and...
An efficient data structure for unbounded bi-objective archives: Introducing the mak_tree
Enhanced temporal difference learning using compiled eligibility traces
Eligibility traces have been shown to substantially improve the convergence speed of temporal...
More effective web search using bigrams and trigrams
This paper investigates the effectiveness of quoted bigrams and trigrams as query terms to target...
Accelerating real-valued genetic algorithms using mutation-with-momentum
An anti-plagiarism editor for software development courses
Concurrent Q-learning: Reinforcement learning for dynamic goals and environments
Global versus local constructive function approximation for on-line reinforcement learning
On-line reinforcement learning using cascade constructive neural networks
The combative accretion model: Multiobjective optimisation without explicit pareto ranking
Contemporary evolutionary multiobjective optimisation techniques are becoming increasingly...
A language for platform independent communication and storage in multiobjective optimisation
Generalised algorithms for redirected walking in virtual environments
Learning place cells from sonar data
LegoTM mindstormsTM robots as a platform for teaching reinforcement learning
PoD can mutate: A simple dynamic directed mutation approach for genetic algorithms
Reducing the time complexity of goal-independent reinforcement learning
Refining search queries from examples using boolean expressions and latent semantic analysis
Adaptive response function neurons
A simplified artificial life model for multiobjective optimisation: A preliminary report
Concurrent Q-learning for autonomous mapping and navigation
A supervised neural network based on the cerebellum