1 citations · 1 across the 1 of their papers we have counts for
3 papers
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★ 1 cited
Am I Building a White Box Agent or Interpreting a Black Box Agent?
Tom Bewley
The rule extraction literature contains the notion of a fidelity-accuracy dilemma: when building an interpretable model of a black box function, optimising for fidelity is likely t…
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…