MARLUI: Multi-Agent Reinforcement Learning for Adaptive UIs

Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user interfaces is the reliance on high-quality user data that has to be collected offline for each task. We formulate UI adaptation as a multi-agent reinforcement learning problem to overcome this challenge. In our formulation, a user agent mimics a real user and learns to interact with a UI. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent learns the task structure from the user agent's behavior and, based on that, can support the user agent in completing its task. Our method produces adaptation policies that are learned in simulation only and, therefore, does not need real user data. Our experiments show that learned policies generalize to real users and achieve on par performance with data-driven supervised learning baselines

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Luxury retail brands and their consumers in emerging markets: developing mobile marketing and sustaining the brand value

Understanding an individual’s self-interests remains a challenging task for consumer marketing because brands have no direct access to individual’s inner mind in order to satisfy his or her consumption-related wants, needs and expectations. In the case of luxury brands, customer service experts only seek to maintain close relationships with wealthy and elite customers, and they cannot extend the same individualized services to mass-market consumers. Among the new middle classes in emerging markets, consumers do not have strong brand attachments, but they do have high purchasing power with regard to luxuries. To bridge this gap, mobile technology could be an ideal interface through which luxury brands could enhance interactive communication and engagement with consumers. Nevertheless, research findings have revealed major discrepancies in the adoption of technology. While luxury brands have been ‘slow’ in their adoption of such technologies, consumers have adopted mobile devices as extensions of themselves in the digital world, which greatly enrich their lifestyles. Therefore, a medium should be developed to bridge this gap. The Gearbox of Exchange is proposed to help integrate the consumer’s self-interests with those of luxury brands. Through conditional access with a mutually agreed-upon exchange value to balance privacy concerns and financial risks, the consumer might be willing to share customized information with the brands with which they trust to engage. The luxury brands will benefit from the sharing of this customized information, as they can better predict an individual’s preferences and choices. This virtual engagement will revitalize customization to activate personalized services for every individual. These mutually agreed-upon interactions will develop into a mutual interdependence, a B2B2C relationship. This bond will protect brands from severe competition. More importantly, their knowledge of customized information, which is provided through their direct access to consumers’ self-interests, will fill the black box of radical behaviourism and enhance these brands’ abilities to predict individual choices. Therefore, the knowledge generated from the Gearbox of Exchange will not be meaningless to transform consumer analysis into micro marketing