Reinforcement learning-driven information seeking: a quantum probabilistic approach
AffiliationUniversity of Bedfordshire
Subject Categories::G510 Information Modelling
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AbstractUnderstanding an information forager’s actions during interaction is very important for the study of interactive information retrieval. Although information spread in an 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 the foragers 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.
CitationJaiswal AK, Liu H, Frommholz I (2020) 'Reinforcement learning-driven information seeking: a quantum probabilistic approach', Bridging the Gap between Information Science, Information Retrieval and Data Science (BIRDS) - Online, CEUR-WS.
TypeConference papers, meetings and proceedings
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