Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach

• URL: http://arxiv.org/abs/2008.02372v1
• Date: Wed, 5 Aug 2020 21:33:51 GMT
• Title: Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach
• Authors: Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
• Abstract summary: An information forager accompanies a piece of information while searching (or foraging) alternative contents, typically subject to decisive uncertainty. We present a reinforcement learning-based framework to model forager exploration that treats the information forager as an agent to guide their behaviour.
• Score: 6.875312133832079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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