Maintaining Evolving Domain Models

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Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation (2018, methodology): “Such models turn out to be especially useful for the representation and learning of human (mental) models from observations, where uncertainty after the learning process can be represented in terms of model annotations as in (Nguyen, Sreedharan, and Kambhampati 2017; Bryce, Benton, and Boldt 2016).”
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy (2017, methodology): “The optimality criterion, however, makes the problem fundamentally different from model change algorithms in [Göbelbecker et al., 2010; Herzig et al., 2014; Bryce et al., 2016] which focus more on the feasibility of plans or correctness of domains.”
Model Recognition as Planning (2019, methodology): “Also, the model drift (Bryce, Benton, and Boldt 2016) approach estimates the error between a computational model, and a ground-truth model which changes as the domain evolves, as the normalized symmetric difference of the propositions that appear in the preconditions and effects of both models.”