18 citations · 37 across the 14 of their papers we have counts for
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cs.AI2020
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…
cs.AI2020
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…
cs.MA2020
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…