Australian Responsible Autonomous Agents Collective

Pursuing excellence in responsible, aligned, and explainable artificial intelligence

Australia’s leading cross-institutional responsible AI collective, envisioning the future of human-agent interaction

Growing reliance on Artificial Intelligence brings new technical, ethical, and humanitarian issues to the forefront.

The Australian Responsible Autonomous Agents Collective (ARAAC) are focused on addressing the technical, ethical, and humanitarian challenges posed by the rise of intelligent systems, in particular systems composed of autonomous agents. A cross-institutional collective of AI researchers, our team specialises in Multi-objective Reinforcement Learning, explainability, transparency, and AI safety, building the theory and practice of balancing AI performance and responsibility.

Our mission is to drive innovation while anchoring our discoveries in responsible and ethical practices. We strive to make AI not just advanced but also accountable, ensuring its alignment with human interests.

Research Leaders

Professor Peter Vamplew

Federation University Australia

Peter is co-founder/co-leader of ARAAC, and a senior member of the Future of Life Institute’s Existential AI safety Research Community. He has played a leading role in establishing multi-objective reinforcement learning (MORL) as a sub-field of reinforcement learning, explicitly designed for problems with multiple conflicting objectives (which describes most real-world problems)

Researcher profile - Peter Vamplew

Professor Richard Dazeley

Deakin University

Richard is the Leader of the Machine Intelligence Lab at Deakin University (Geelong), and the Deputy Head of School. He is a leading researcher in the Human-alignment of autonomous agents through Safe, Ethical, Explainable and Interactive methods utilising Multiobjective Reinforcement Learning (MORL) and is a senior member of the AI existential Safety Community

Researcher profile - Richard Dazeley


Dr Bahar Nakisa

Deakin University

Dr. Bahareh Nakisa is a Lecturer of Applied AI and the course director of Applied AI at School of Information Technology, Deakin University. Bahar’s expertise spans multiple domains, encompassing applied AI, deep learning, computer vision, affective computing, and human-aligned AI in autonomous systems.

Researcher profile - Bahar Nakisa

Dr Cameron Foale

Federation University Australia

Cameron has an interest in building usable, fair, transparent, and scalable connected eHealth systems, and applying AI techniques to time-series data.

Researcher profile - Cameron Foale

Dr Francisco Cruz


Francisco’s current research interests include reinforcement learning, explainable artificial intelligence, human-robot interaction, artificial neural networks, and psychologically and bio-inspired models.

Researcher profile - Francisco Cruz

Dr Mahdi Kazemi Moghaddam

Deakin University

Mahdi was a Research Fellow in Reinforcement Learning at Deakin University from 2022-2023. Mahdi is interested in (deep) reinforcement learning, focusing on both single-agent and multi-agent systems. He strives to contribute to the advancement of responsible AI solutions by developing methods that prioritise fairness and trust without compromising efficiency.

Researcher profile - Mahdi Kazemi Moghaddam

Dr Sunil Aryal

Deakin University

Sunil works in a wide range of areas in ML/AI from similarity measures, anomaly detection, classification, clustering, text/image analysis, ensemble learning, learning from small subsamples of data, and robust/explainable machine learning to autonomous systems.

Researcher profile - Sunil Aryal


Adrian Ly

Deakin University

Adrian Ly received his bachelor’s degree in commerce from the University of Melbourne and a master’s degree in data science from Deakin University. Currently he is completing his Doctor of Philosophy with a focus in reinforcement learning with Deakin University and working in industry as a data scientist.

Researcher profile - Adrian Ly

Alex England

Deakin University

Alex is a motivated software engineer with a balanced mix of practical experience and formal qualifications. His research focuses on software development methodologies and ethics for Artificial Intelligence systems.

Researcher profile - Alex England

Hadassah Harland (Haddie)

Deakin University

Haddie is a PhD student at Deakin University (Geelong) and Top-Up Scholarship recipient with CSIRO’s Data61 Robotics and Autonomous Systems Group, with an interest in Human-Machine Collaboration. Haddie is currently researching approaches to socially responsible, interactive and human-aligned autonomous agents through the lens of apology, including implementation utilising multi-objective reinforcement learning (MORL).

Researcher profile - Hadassah Harland (Haddie)

Nikolaj Goodger

Federation University Australia

Nikolaj is currently a PhD student studying using the use of language in Reinforcement Learning. He is primarily interested in model-free reinforcement learning and generalisation in machine learning.

Researcher profile - Nikolaj Goodger

Scott Johnson

Deakin University

Scott is currently studying for his Honours degree at Deakin University, with a focus on the transfer of safety knowledge between environments using Multi-Objective Reinforcement Learning. He has worked as a research assistant on several ML projects for both Deakin University and Federation University.

Researcher profile - Scott Johnson

Past Staff, Students and Visitors

Ethan Watkins (EJ)


EJ is a chemist by training but has pivoted his career towards AI safety research to ensure that advances in AI result in human flourishing. He is particularly interested in Reinforcement Learning and is excited to explore the potential of multi-objective approaches to train agents that are better aligned with human goals. He is currently working with ARAAC researchers as an intern.

Researcher profile - Ethan Watkins (EJ)

Yifei Chen

University of Groningen, the Netherlands

Yifei Chen is a Ph.D. candidate at the University of Groningen, the Netherlands, and a visiting researcher in the lab of Dr. Francisco Cruz at UNSW during 2023. Before her Ph.D., Yifei received her master’s degree from Zhejiang University, China. Yifei’s research mainly focuses on fundamental reinforcement learning, especially methods to improve efficiency and reduce the bias of reinforcement learning algorithms. She is also interested in applying her methods to robotics.

Researcher profile - Yifei Chen