5 papers
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection
Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry
A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants o…
Reward Learning with Trees: Methods and Evaluation
Tom Bewley, Jonathan Lawry, Arthur Richards +2
Recent efforts to learn reward functions from human feedback have tended to use deep neural networks, whose lack of transparency hampers our ability to explain agent behaviour or v…
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
Tom Bewley, Jonathan Lawry
In explainable artificial intelligence, there is increasing interest in understanding the behaviour of autonomous agents to build trust and validate performance. Modern agent archi…
Modelling Agent Policies with Interpretable Imitation Learning
Tom Bewley, Jonathan Lawry, Arthur Richards
As we deploy autonomous agents in safety-critical domains, it becomes important to develop an understanding of their internal mechanisms and representations. We outline an approach…
Distributed Possibilistic Learning in Multi-Agent Systems
Jonathan Lawry, Michael Crosscombe, David Harvey
Possibility theory is proposed as an uncertainty representation framework for distributed learning in multi-agent systems and robot swarms. In particular, we investigate its applic…