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Found 3 papers, 1 papers with code
On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios
In this paper, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations.
On Exploiting Hitting Sets for Model Reconciliation
A popular approach to do this is called model reconciliation, where the agent tries to reconcile the differences in its model and the human's model such that the plan is also optimal in the human's model.
Machine Reasoning Explainability
As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning.
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