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![]() Title:Data Efficient Paradigms for Personalized Assessment of Taskable AI Systems Authors:Pulkit Verma Conference:KR 2022 Tags:action model learning, agent interrogation and model-based reasoning Abstract: The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop algorithms and requirements of interpretability that would enable a user to assess and understand the limits of an AI system’s safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer the queries about its execution of sequences of actions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system’s capabilities in fully observable and deterministic settings. Data Efficient Paradigms for Personalized Assessment of Taskable AI Systems ![]() Data Efficient Paradigms for Personalized Assessment of Taskable AI Systems | ||||
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