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Reinforcement learning-driven information seeking: a quantum probabilistic approach

  • Amit Kumar Jaiswal
    ,
  • Haiming Liu
    ,
  • Ingo Frommholz
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Open access

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.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 16-29

Publication milestones

  • Published - 30/07/2020

Publication status

Published - 30/07/2020

Volume

2741

Publisher

CEUR-WS, Germany

Publication IDs

  • handle.net: 10547/625259
  • Scopus: 85098987403

Host publication title

Bridging the Gap between Information Science, Information Retrieval and Data Science (BIRDS) 2020

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