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

• URL: http://arxiv.org/abs/2405.09255v1
• Date: Wed, 15 May 2024 11:14:33 GMT
• Title: Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces
• Authors: Daniel Gaspar-Figueiredo, Marta Fernández-Diego, Ruben Nuredini, Silvia Abrahão, Emilio Insfrán,
• Abstract summary: Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization [3.1944843830667766]
  Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge. We present Adaptive Self-Supervised Learning Strategies (ASLS), which utilize self-supervised learning techniques to personalize LLMs dynamically.
  arXiv  Detail & Related papers  (2024-09-25T14:35:06Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• Learning from Interaction: User Interface Adaptation using Reinforcement Learning [0.0]
  This thesis proposes an RL-based UI adaptation framework that uses physiological data. The framework aims to learn from user interactions and make informed adaptations to improve user experience (UX)
  arXiv  Detail & Related papers  (2023-12-12T12:29:18Z)
• A Comparative Study on Reward Models for UI Adaptation with Reinforcement Learning [0.6899744489931015]
  Reinforcement learning can be used to personalise interfaces for each context of use. determining the reward of each adaptation alternative is a challenge in RL for UI adaptation. Recent research has explored the use of reward models to address this challenge, but there is currently no empirical evidence on this type of model.
  arXiv  Detail & Related papers  (2023-08-26T18:31:16Z)
• Computational Adaptation of XR Interfaces Through Interaction Simulation [4.6193503399184275]
  We discuss a computational approach to adapt XR interfaces with the goal of improving user experience and performance. Our novel model, applied to menu selection tasks, simulates user interactions by considering both cognitive and motor costs.
  arXiv  Detail & Related papers  (2022-04-19T23:37:07Z)
• Adapting User Interfaces with Model-based Reinforcement Learning [47.469980921522115]
  Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy.
  arXiv  Detail & Related papers  (2021-03-11T17:24:34Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Optimizing Interactive Systems via Data-Driven Objectives [70.3578528542663]
  We propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. We introduce Interactive System (ISO), a novel algorithm that uses these inferred objectives for optimization.
  arXiv  Detail & Related papers  (2020-06-19T20:49:14Z)
• Empowering Active Learning to Jointly Optimize System and User Demands [70.66168547821019]
  We propose a new active learning approach that jointly optimize the active learning system (training efficiently) and the user (receiving useful instances) We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
  arXiv  Detail & Related papers  (2020-05-09T16:02:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.