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Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces

Daniel Gaspar-Figueiredo, Marta Fernández-Diego, Ruben Nuredini, Silvia Abrahão, Emilio Insfrán

TL;DR

This work tackles the challenge of adapting user interfaces to dynamic user needs and contexts by proposing a reinforcement-learning framework that learns UI adaptations over time. It provides a configurable Intelligent UI Adaptation Framework with a Gym-based environment and uses predictive HCI models to reward RL-driven improvements in engagement, integrating an RL agent within an MDP. The main contributions include the concrete instantiation of the framework, the OpenAI Gym-based implementation, and a simulated evaluation demonstrating that the RL agent can learn effective adaptation strategies and achieve high engagement while balancing general user tendencies and individual preferences. The findings highlight the framework's configurability and potential for real-world application, while also outlining avenues for future work in richer state representations, alternative RL algorithms, and validation with real users.

Abstract

Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. The main challenge is to provide the appropriate adaptations at the appropriate time to offer value to end-users. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component to adapt user interfaces and ultimately improving the overall User Experience (UX). By using RL, the system is able to learn from past adaptations to improve the decision-making capabilities. Moreover, assessing the success of such adaptations remains a challenge. To overcome this issue, we propose to use predictive Human-Computer Interaction (HCI) models to evaluate the outcome of each action (ie adaptations) performed by the RL agent. In addition, we present an implementation of the instantiated framework, which is an extension of OpenAI Gym, that serves as a toolkit for developing and comparing RL algorithms. This Gym environment is highly configurable and extensible to other UI adaptation contexts. The evaluation results show that our RL-based framework can successfully train RL agents able to learn how to adapt UIs in a specific context to maximize the user engagement by using an HCI model as rewards predictor.

Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces

TL;DR

This work tackles the challenge of adapting user interfaces to dynamic user needs and contexts by proposing a reinforcement-learning framework that learns UI adaptations over time. It provides a configurable Intelligent UI Adaptation Framework with a Gym-based environment and uses predictive HCI models to reward RL-driven improvements in engagement, integrating an RL agent within an MDP. The main contributions include the concrete instantiation of the framework, the OpenAI Gym-based implementation, and a simulated evaluation demonstrating that the RL agent can learn effective adaptation strategies and achieve high engagement while balancing general user tendencies and individual preferences. The findings highlight the framework's configurability and potential for real-world application, while also outlining avenues for future work in richer state representations, alternative RL algorithms, and validation with real users.

Abstract

Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. The main challenge is to provide the appropriate adaptations at the appropriate time to offer value to end-users. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component to adapt user interfaces and ultimately improving the overall User Experience (UX). By using RL, the system is able to learn from past adaptations to improve the decision-making capabilities. Moreover, assessing the success of such adaptations remains a challenge. To overcome this issue, we propose to use predictive Human-Computer Interaction (HCI) models to evaluate the outcome of each action (ie adaptations) performed by the RL agent. In addition, we present an implementation of the instantiated framework, which is an extension of OpenAI Gym, that serves as a toolkit for developing and comparing RL algorithms. This Gym environment is highly configurable and extensible to other UI adaptation contexts. The evaluation results show that our RL-based framework can successfully train RL agents able to learn how to adapt UIs in a specific context to maximize the user engagement by using an HCI model as rewards predictor.
Paper Structure (23 sections, 2 equations, 2 figures)

This paper contains 23 sections, 2 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Intelligent User Interface adaptation Framework
  4. Reinforcement Learning Agent to adapt User Interfaces
  5. Markov Decision Process Definition
  6. States
  7. Actions
  8. Transition probabilities
  9. Reward
  10. Implementation
  11. Setup
  12. States
  13. Actions
  14. Reward
  15. Algorithm
  16. ...and 8 more sections

Figures (2)

  • Figure 1: User Interface Adaptation framework using Reinforcement Learning. This is an extension from the original conceptual framework abrahaoModel:2021.
  • Figure 2: RL agent learning process. a) The number of steps needed to finish an episode decreases over time; b) The score increases over time and the agent converges to an optimal solution; c) RL agent evaluation process.