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 paradigms 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 queries about these executions. 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 settings.
Keywords
- Planning and Scheduling: PS: Learning in planning and scheduling
- Knowledge Representation and Reasoning: KRR: Learning and reasoning
- Knowledge Representation and Reasoning: KRR: Reasoning about actions
- Planning and Scheduling: PS: Model-based reasoning
