Can machines be uncertain?
It addresses the problem of modeling uncertainty in AI for researchers, but is incremental as it builds on existing philosophical and computational concepts.
The paper investigates how AI systems can realize states of uncertainty, distinguishing between epistemic and subjective uncertainty, and proposes that some uncertainty states are interrogative attitudes with questions as content.
The paper investigates whether and how AI systems can realize states of uncertainty. By adopting a functionalist and behavioral perspective, it examines how symbolic, connectionist and hybrid architectures make room for uncertainty. The paper distinguishes between epistemic uncertainty, or uncertainty inherent in the data or information, and subjective uncertainty, or the system's own attitude of being uncertain. It further distinguishes between distributed and discrete realizations of subjective uncertainty. A key contribution is the idea that some states of uncertainty are interrogative attitudes whose content is a question rather than a proposition.