Benjamin RosmanEnquire
Research

How do agents learn
to act in the world?

My work explores learning, abstraction and decision making in autonomous systems, with a growing focus on safety and meaningful interaction with people.

Research questions

Learning that carries forward.

An intelligent agent should be able to use what it has learned before, make decisions under uncertainty and adapt when the world changes.

At the Robotics, Autonomous Intelligence and Learning (RAIL) Lab at Wits, we investigate reinforcement learning, transfer and composition, multi-agent interaction, robotics and AI safety. The work is grounded in technical questions, with consequences for how intelligent systems operate among people.

Visit RAIL Lab ↗Google Scholar ↗
Themes

Across the lab and beyond.

01

Reinforcement learning

How agents can learn from interaction and reuse knowledge across related tasks.

02

Decision making under uncertainty

How autonomous systems choose actions when information is incomplete or conditions change.

03

Safety for autonomous agents

How to evaluate and constrain agents that learn and act over time in environments shared with people.

04

Science of intelligence

Connecting questions about machine, human and animal intelligence through the Wits MIND Institute.

Publications

Selected work and the full record.

This is a small selection. For recent papers and co-authored work, see the full publication record.

Full publication record
Books and chapters

Ideas beyond the lab.

Contributions to books on AI sovereignty, machine consciousness and ethical practice.

At Wits

Research is a collective endeavour.

RAIL brings together students and collaborators working on intelligent agents. MIND creates a broader home for interdisciplinary research into intelligence and its impact on society.

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