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It is essential for users to understand what their AI systems can and can'tdo in order to use them safely. However, the problem of enabling users toassess AI systems with sequential decision-making (SDM) capabilities isrelatively understudied. This paper presents a new approach for modeling thecapabilities of black-box AI systems that can plan and act, along with thepossible effects and requirements for executing those capabilities instochastic settings. We present an active-learning approach that caneffectively interact with a black-box SDM system and learn an interpretableprobabilistic model describing its capabilities. Theoretical analysis of theapproach identifies the conditions under which the learning process isguaranteed to converge to the correct model of the agent; empirical evaluationson different agents and simulated scenarios show that this approach is few-shotgeneralizable and can effectively describe the capabilities of arbitraryblack-box SDM agents in a sample-efficient manner.

Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)