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Dr Cameron Foale

Lecturer, Information Technology

School of Engineering, IT and Phys. Sci.

Section/Portfolio:

Information Technology (Ballarat)

Location:

Mt Helen Campus, Online

Biography

Dr Cameron Foale is a Lecturer in information technology at Federation University Australia. Dr Foale’s research expertise includes digital capture of stringed instrument acoustics, biopsychosocial health data, reinforcement learning, and safe and explainable artificial intelligence.

Cameron is currently working on research projects in machine learning for cross-talk minimisation, Bayesian approaches to multiobjective reinforcement learning, and multi-channel effects processing for stringed instruments. Cameron is an active member of Federation Learning Agents Group (FLAG) and has been part of several of the University’s flagship research centres, as well as being involved in the Internet Commerce Security Laboratory (ICSL).

Cameron completed his PhD at the University of Ballarat in 2010 in the field of acoustics for virtual environments, and subsequently entered the IT industry as a web and games developer in the digital education sector. Cameron returned to teaching and research at Federation University in 2014.

An evaluation methodology for interactive reinforcement learning with simulated users

Interactive reinforcement learning methods utilise an external information source to evaluate...

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...

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

A common approach to address multiobjective problems using reinforcement learning methods is to...

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...

Towards Machine Learning approach for Digital-Health intervention program

Digital-Health intervention (DHI) are used by health care providers to promote engagement...

  • Conference Proceedings

Human-aligned artificial intelligence is a multiobjective problem

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to...

Modeling neurocognitive reaction time with Gamma distribution

As a broader effort to build a holistic biopsychosocial health metric, reaction time data...

Non-functional regression: A new challenge for neural networks

This work identifies an important, previously unaddressed issue for regression based on neural...

Relevance of Frequency of Heart-Rate Peaks as Indicator of 'Biological' Stress Level

The biopsychosocial (BPS) model proposes that health is best understood as a combination of...

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,...

Softmax exploration strategies for multiobjective reinforcement learning

Despite growing interest over recent years in applying reinforcement learning to multiobjective...

Steering approaches to Pareto-optimal multiobjective reinforcement learning

For reinforcement learning tasks with multiple objectives, it may be advantageous to learn...

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...

Reinforcement learning of pareto-optimal multiobjective policies using steering

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...

  • Conference Proceedings