Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach

TL;DR

This paper models information seeking as a reinforcement learning problem, using quantum mechanics principles to handle the inherent uncertainty in user interactions within complex information spaces.

Contribution

It introduces a novel quantum probabilistic framework for reinforcement learning-based information foraging, capturing uncertainty in user behavior during information retrieval.

Findings

Framework effectively models uncertainty in user actions.

Quantum formalism improves understanding of information foraging.

Potential for enhanced interactive information retrieval systems.

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.