4 papers
The Mirror Agent Model: a Bayesian Architecture for Interpretable Agent Behavior
Michele Persiani, Thomas Hellström
In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer to this architecture as the Mirror Agent Model because it defines the…
Towards a Foundational Ontology for Identifying and Resolving Contradictions in Dialogue-based Human-Robot Interactions
Maitreyee Tewari, Michele Persiani
Existing Human-Robot Interaction (HRI) literature has focused on identifying and structuring errors, failures, conflicts, and knowledge issues (called in this work as contradiction…
Informative Communication of Robot Plans
Michele Persiani, Thomas Hellstrom
When a robot is asked to verbalize its plan it can do it in many ways. For example, a seemingly natural strategy is incremental, where the robot verbalizes its planned actions in p…
Policy Regularization for Legible Behavior
Michele Persiani, Thomas Hellström
In Reinforcement Learning interpretability generally means to provide insight into the agent's mechanisms such that its decisions are understandable by an expert upon inspection. T…