Associate Professor Cameron Foale
Federation University Australia
Dr Cameron Foale is a lecturer and researcher at Federation University, with an interest in building usable, fair, transparent, and scalable connected eHealth systems. His current research projects consider how statistics, digital signal processing and artificial intelligence can be applied to time-series data, gathered from either non-linear systems or self-reported health data. Dr Foale also works closely with ARAAC members in human-aligned and multiobjective reinforcement learning and artificial intelligence. He has spent significant time in industry, and has a deep understanding of how to bring ideas from conception to production.
ARAAC Publications
- (2025). An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments The Knowledge Engineering Review
- (2024). Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
- (2023). A NetHack Learning Environment Language Wrapper for Autonomous Agents Journal of Open Research Software
- (2023). Human engagement providing evaluative and informative advice for interactive reinforcement learning Neural Computing and Applications
- (2023). Persistent rule-based interactive reinforcement learning Neural Computing and Applications
- (2022). The impact of environmental stochasticity on value-based multiobjective reinforcement learning Neural Computing and Applications
- (2022). Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021) Autonomous Agents and Multi-Agent Systems (JAAMAS)
- (2021). Language Representations for Generalization in Reinforcement Learning Asian Conference on Machine Learning (ACML)
- (2021). An evaluation methodology for interactive reinforcement learning with simulated users Biomimetics
- (2021). A conceptual framework for externally-influenced agents: an assisted reinforcement learning review Journal of Ambient Intelligence and Humanized Computing
- (2021). Levels of explainable artificial intelligence for human-aligned conversational explanations Artificial Intelligence
- (2021). Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety Engineering Applications of Artificial Intelligence
- (2018). Human-aligned artificial intelligence is a multiobjective problem Ethics and Information Technology
- (2017). Steering approaches to Pareto-optimal multiobjective reinforcement learning Neurocomputing
- (2017). Softmax exploration strategies for multiobjective reinforcement learning Neurocomputing