Jaiswal, AK orcid.org/0000-0001-8848-7041, Liu, H and Frommholz, I (2020) Reinforcement learning-driven information seeking: A quantum probabilistic approach. In: CEUR Workshop Proceedings. Bridging the Gap between Information Science, Information Retrieval and Data Science (BIRDS 2020), 30 Jul 2020, Online. CEUR Workshop Proceedings , pp. 16-29.
Abstract
Understanding 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 for this paper by its authors. Use permitted under CreativeCommons License Attribution 4.0 International (CC BY 4.0). BIRDS 2020, 30 July2020, Xi’an, China (online) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Apr 2021 12:54 |
Last Modified: | 11 May 2021 20:03 |
Status: | Published |
Publisher: | CEUR Workshop Proceedings |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173380 |