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Heuristic Recognition and Rapid Response to Unfamiliar Events Outside of Agent Design Scope

Regardless of past learning, an agent in an open world will face unfamiliar situations and events outside of prior experience, existing models, or policies. Further, the agent will sometimes lack relevant knowledge and/or sufficient time to assess the situation, generate and evaluate options, and pursue a robustly considered course of action. How can an agent respond reasonably to situations that are outside of its original design scope? How can it recognize such situations sufficiently quickly and reliably to determine reasonable, adaptive courses of action? We identify key characteristics needed for solutions, evaluate the state-of-the-art by these requirements, and outline a proposed, novel approach that combines domain-general meta-knowledge (in the form of appraisals inspired by human cognition) and metareasoning. It has the potential to provide fast, adaptive responses to unfamiliar situations, more fully meeting the performance characteristics required for open-world, general agents.

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