  Understanding an information forager's actions during interaction is very
important for the study of interactive information retrieval. Although
information spread in uncertain information space is substantially complex due
to the high entanglement of users interacting with information objects~(text,
image, etc.). However, an information forager, in general, accompanies a piece
of information (information diet) while searching (or foraging) alternative
contents, typically subject to decisive uncertainty. Such types of uncertainty
are analogous to measurements in quantum mechanics which follow the uncertainty
principle. In this paper, we discuss information seeking as a reinforcement
learning task. We then present a reinforcement learning-based framework to
model forager exploration that treats the information forager as an agent to
guide their behaviour. Also, our framework incorporates the inherent
uncertainty of the foragers' action using the mathematical formalism of quantum
mechanics.
