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Agent explanation generation is the task of justifying the decisions of an agent after observing its behaviour. Much of the previous explanation generation approaches can theoretically do so, but assuming the availability of explanation generation modules, reliable observations, and deterministic execution of plans. However, in real-life settings, explanation generation modules are not readily available, unreliable observations are frequently encountered, and plans are non-deterministic. We seek in this work to address these challenges. This work presents a data-driven approach to mining and v...

Open environments require dynamic execution of plans where agents must engage in settings that include, for example, re-planning, plan reusing, plan repair, etc. Hence, real-life Plan Recognition (PR) approaches are required to deal with different classes of observations (e.g., exogenous actions, switching between activities, and missing observations). Many approaches to PR consider these classes of observations, but none have dealt with them as deliberated events. Actually, using existing PR methods to explain such classes of observations may generate only so-called imperfect explanations (pl...

A Digital Twin ideally manifests the same behaviour (in silico) of its physical counterpart. While considerable attention has been paid to the development of Digital Twins for physical devices/systems, the question of developing Twins for organizations has received relatively little attention. The setting in which we address this problem is very general and, consequently, very challenging. We look at the automatic acquisition of Digital Twins of organizations. To that end, this paper builds on the following two premises: (1) that Digital Twins of organizations can provide value, and (2) that B...