4,992 research outputs found
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
Reinforcement Learning Approaches in Social Robotics
This article surveys reinforcement learning approaches in social robotics.
Reinforcement learning is a framework for decision-making problems in which an
agent interacts through trial-and-error with its environment to discover an
optimal behavior. Since interaction is a key component in both reinforcement
learning and social robotics, it can be a well-suited approach for real-world
interactions with physically embodied social robots. The scope of the paper is
focused particularly on studies that include social physical robots and
real-world human-robot interactions with users. We present a thorough analysis
of reinforcement learning approaches in social robotics. In addition to a
survey, we categorize existent reinforcement learning approaches based on the
used method and the design of the reward mechanisms. Moreover, since
communication capability is a prominent feature of social robots, we discuss
and group the papers based on the communication medium used for reward
formulation. Considering the importance of designing the reward function, we
also provide a categorization of the papers based on the nature of the reward.
This categorization includes three major themes: interactive reinforcement
learning, intrinsically motivated methods, and task performance-driven methods.
The benefits and challenges of reinforcement learning in social robotics,
evaluation methods of the papers regarding whether or not they use subjective
and algorithmic measures, a discussion in the view of real-world reinforcement
learning challenges and proposed solutions, the points that remain to be
explored, including the approaches that have thus far received less attention
is also given in the paper. Thus, this paper aims to become a starting point
for researchers interested in using and applying reinforcement learning methods
in this particular research field
Towards Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning
The next step for intelligent dialog agents is to escape their role as silent
bystanders and become proactive. Well-defined proactive behavior may improve
human-machine cooperation, as the agent takes a more active role during
interaction and takes off responsibility from the user. However, proactivity is
a double-edged sword because poorly executed pre-emptive actions may have a
devastating effect not only on the task outcome but also on the relationship
with the user. For designing adequate proactive dialog strategies, we propose a
novel approach including both social as well as task-relevant features in the
dialog. Here, the primary goal is to optimize proactive behavior so that it is
task-oriented - this implies high task success and efficiency - while also
being socially effective by fostering user trust. Including both aspects in the
reward function for training a proactive dialog agent using reinforcement
learning showed the benefit of our approach for more successful human-machine
cooperation
MeIPeAS: An Intelligent Virtual Tutor for Mexican Elementary Schoolchildren
ArtÃculo en revista indizada publicado en Research in Computing ScienceIt is known that virtual tutors have a wide range of functionalities, which have been little exploited and applied in the educational field at the primary level. However, these functionalities allow to offer mechanisms of interaction with students through an interactive dialogue by using text to speech, and even more sophisticated, the recognition and understanding of natural language or speech. In this paper, a personalized virtual tutor for the primary education scenario in Mexico is presented. This virtual tutor is called Mexican Intelligent Pedagogical Agent for Schoolchildren (MeIPeAS) and was created to be used as a pedagogical support mechanism offering a unique attraction for current and future generations of schoolchildren in Mexico. The virtual tutor has been validated in practice in public primary schools of the municipalities of the State of Mexico in Mexico. This validation is to analyze the impact of the user experience from the obtained results having relevant information about the reinforcement of topics taught within the classroom
Conversational affective social robots for ageing and dementia support
Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective
AI in Learning: Designing the Future
AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers
Future trends and research issues of technology-enhanced language learning: A technological perspective
With recent advancements in information technologies and language learning models, rapid innovations of technology-enhanced language learning have been widely witnessed by research communities and educational institutions globally. Powerful new technologies, such as social media and networks, mobile applications, wearable computing, cloud computing, and virtual reality have been integrated into language learning to facilitate various aspects, such as interactivity, immediacy, and authenticity. In this study, we present the Future TELL Model considering learning objectives, theories, and strategies by briefly reviewing recent progresses in this area. Future trends and research issues in technology-enhanced language learning are also discussed in relation to cutting-edge technologies, such as deep neural networks, which have not yet been fully recognized by education technology communities
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