User Modeling and User-Adapted Interaction最新文献_第7页

A dichotomic approach to adaptive interaction for socially assistive robots. 社会辅助机器人自适应交互的二分类方法。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-022-09347-6

Riccardo De Benedictis, Alessandro Umbrico, Francesca Fracasso, Gabriella Cortellessa, Andrea Orlandini, Amedeo Cesta

{"title":"A dichotomic approach to adaptive interaction for socially assistive robots.","authors":"Riccardo De Benedictis, Alessandro Umbrico, Francesca Fracasso, Gabriella Cortellessa, Andrea Orlandini, Amedeo Cesta","doi":"10.1007/s11257-022-09347-6","DOIUrl":"https://doi.org/10.1007/s11257-022-09347-6","url":null,"abstract":"Socially assistive robotics (SAR) aims at designing robots capable of guaranteeing social interaction to human users in a variety of assistance scenarios that range, e.g., from giving reminders for medications to monitoring of Activity of Daily Living, from giving advices to promote an healthy lifestyle to psychological monitoring. Among possible users, frail older adults deserve a special focus as they present a rich variability in terms of both alternative possible assistive scenarios (e.g., hospital or domestic environments) and caring needs that could change over time according to their health conditions. In this perspective, robot behaviors should be customized according to properly designed user models. One of the long-term research goals for SAR is the realization of robots capable of, on the one hand, personalizing assistance according to different health-related conditions/states of users and, on the other, adapting behaviors according to heterogeneous contexts as well as changing/evolving needs of users. This work proposes a solution based on a user model grounded on the international classification of functioning, disability and health (ICF) and a novel control architecture inspired by the dual-process theory. The proposed approach is general and can be deployed in many different scenarios. In this paper, we focus on a social robot in charge of the synthesis of personalized training sessions for the cognitive stimulation of older adults, customizing the adaptive verbal behavior according to the characteristics of the users and to their dynamic reactions when interacting. Evaluations with a restricted number of users show good usability of the system, a general positive attitude of users and the ability of the system to capture users personality so as to adapt the content accordingly during the verbal interaction.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9380760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An adaptive decision-making system supported on user preference predictions for human-robot interactive communication. 基于用户偏好预测的自适应决策系统，用于人机互动交流。

IF 3 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 Epub Date: 2022-04-09 DOI: 10.1007/s11257-022-09321-2

Marcos Maroto-Gómez, Álvaro Castro-González, José Carlos Castillo, María Malfaz, Miguel Ángel Salichs

{"title":"An adaptive decision-making system supported on user preference predictions for human-robot interactive communication.","authors":"Marcos Maroto-Gómez, Álvaro Castro-González, José Carlos Castillo, María Malfaz, Miguel Ángel Salichs","doi":"10.1007/s11257-022-09321-2","DOIUrl":"10.1007/s11257-022-09321-2","url":null,"abstract":"Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this context, a social robot that adapts its behaviour to different users and proactively suggests their favourite activities may produce a more successful interaction. In this work, we describe how the autonomous decision-making system embedded in our social robot Mini can produce a personalised interactive communication experience by considering the preferences of the user the robot interacts with. We compared the performance of Top Label as Class and Ranking by Pairwise Comparison, two promising algorithms in the area, to find the one that best predicts the user preferences. Although both algorithms provide robust results in preference prediction, we decided to integrate Ranking by Pairwise Comparison since it provides better estimations. The method proposed in this contribution allows the autonomous decision-making system of the robot to work on different modes, balancing activity exploration with the selection of the favourite entertaining activities. The operation of the preference learning system is shown in three real case studies where the decision-making system works differently depending on the user the robot is facing. Then, we conducted a human-robot interaction experiment to investigate whether the robot users perceive the personalised selection of activities more appropriate than selecting the activities at random. The results show how the study participants found the personalised activity selection more appropriate, improving their likeability towards the robot and how intelligent they perceive the system. query Please check the edit made in the article title.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9372877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalised socially assistive robot for cardiac rehabilitation: Critical reflections on long-term interactions in the real world. 心脏康复的个性化社会辅助机器人:对现实世界中长期互动的批判性反思。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-022-09323-0

Bahar Irfan, Nathalia Céspedes, Jonathan Casas, Emmanuel Senft, Luisa F Gutiérrez, Mónica Rincon-Roncancio, Carlos A Cifuentes, Tony Belpaeme, Marcela Múnera

{"title":"Personalised socially assistive robot for cardiac rehabilitation: Critical reflections on long-term interactions in the real world.","authors":"Bahar Irfan, Nathalia Céspedes, Jonathan Casas, Emmanuel Senft, Luisa F Gutiérrez, Mónica Rincon-Roncancio, Carlos A Cifuentes, Tony Belpaeme, Marcela Múnera","doi":"10.1007/s11257-022-09323-0","DOIUrl":"https://doi.org/10.1007/s11257-022-09323-0","url":null,"abstract":"Lack of motivation and low adherence rates are critical concerns of long-term rehabilitation programmes, such as cardiac rehabilitation. Socially assistive robots are known to be effective in improving motivation in therapy. However, over longer durations, generic and repetitive behaviours by the robot often result in a decrease in motivation and engagement, which can be overcome by personalising the interaction, such as recognising users, addressing them with their name, and providing feedback on their progress and adherence. We carried out a real-world clinical study, lasting 2.5 years with 43 patients to evaluate the effects of using a robot and personalisation in cardiac rehabilitation. Due to dropouts and other factors, 26 patients completed the programme. The results derived from these patients suggest that robots facilitate motivation and adherence, enable prompt detection of critical conditions by clinicians, and improve the cardiovascular functioning of the patients. Personalisation is further beneficial when providing high-intensity training, eliciting and maintaining engagement (as measured through gaze and social interactions) and motivation throughout the programme. However, relying on full autonomy for personalisation in a real-world environment resulted in sensor and user recognition failures, which caused negative user perceptions and lowered the perceived utility of the robot. Nonetheless, personalisation was positively perceived, suggesting that potential drawbacks need to be weighed against various benefits of the personalised interaction.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9437126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Justification of recommender systems results: a service-based approach. 推荐系统结果的证明:基于服务的方法。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-022-09345-8

Noemi Mauro, Zhongli Filippo Hu, Liliana Ardissono

{"title":"Justification of recommender systems results: a service-based approach.","authors":"Noemi Mauro, Zhongli Filippo Hu, Liliana Ardissono","doi":"10.1007/s11257-022-09345-8","DOIUrl":"https://doi.org/10.1007/s11257-022-09345-8","url":null,"abstract":"With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during the overall interaction with an item, from its selection to its usage. Thus, they cannot assess their impact on the user's experience. To address this issue, we propose a novel justification approach that uses service models to (i) extract experience data from reviews concerning all the stages of interaction with items, at different granularity levels, and (ii) organize the justification of recommendations around those stages. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. The participants evaluated the Perceived User Awareness Support provided by our service-based justification models higher than the one offered by the baselines. Moreover, our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC). Differently, high NfC participants preferred a direct inspection of item reviews. These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9504943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Enhancing a student productivity model for adaptive problem-solving assistance. 增强学生生产力模型以适应问题解决的协助。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-022-09338-7

Mehak Maniktala, Min Chi, Tiffany Barnes

{"title":"Enhancing a student productivity model for adaptive problem-solving assistance.","authors":"Mehak Maniktala, Min Chi, Tiffany Barnes","doi":"10.1007/s11257-022-09338-7","DOIUrl":"https://doi.org/10.1007/s11257-022-09338-7","url":null,"abstract":"Research on intelligent tutoring systems has been exploring data-driven methods to deliver effective adaptive assistance. While much work has been done to provide adaptive assistance when students seek help, they may not seek help optimally. This had led to the growing interest in proactive adaptive assistance, where the tutor provides unsolicited assistance upon predictions of struggle or unproductivity. Determining when and whether to provide personalized support is a well-known challenge called the assistance dilemma. Addressing this dilemma is particularly challenging in open-ended domains, where there can be several ways to solve problems. Researchers have explored methods to determine when to proactively help students, but few of these methods have taken prior hint usage into account. In this paper, we present a novel data-driven approach to incorporate students' hint usage in predicting their need for help. We explore its impact in an intelligent tutor that deals with the open-ended and well-structured domain of logic proofs. We present a controlled study to investigate the impact of an adaptive hint policy based on predictions of HelpNeed that incorporate students' hint usage. We show empirical evidence to support that such a policy can save students a significant amount of time in training and lead to improved posttest results, when compared to a control without proactive interventions. We also show that incorporating students' hint usage significantly improves the adaptive hint policy's efficacy in predicting students' HelpNeed, thereby reducing training unproductivity, reducing possible help avoidance, and increasing possible help appropriateness (a higher chance of receiving help when it was likely to be needed). We conclude with suggestions on the domains that can benefit from this approach as well as the requirements for adoption.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9362072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10772089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design, development, and evaluation of an interactive personalized social robot to monitor and coach post-stroke rehabilitation exercises. 设计、开发和评估用于监测和指导中风后康复训练的交互式个性化社交机器人。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-022-09348-5

Min Hun Lee, Daniel P Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia

{"title":"Design, development, and evaluation of an interactive personalized social robot to monitor and coach post-stroke rehabilitation exercises.","authors":"Min Hun Lee, Daniel P Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia","doi":"10.1007/s11257-022-09348-5","DOIUrl":"https://doi.org/10.1007/s11257-022-09348-5","url":null,"abstract":"Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these systems still remains a challenge. In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation. Through interviews with therapists, we designed how this system interacts with the user and then developed an interactive social robot exercise coaching system. This system integrates a neural network model with a rule-based model to automatically monitor and assess patients' rehabilitation exercises and can be tuned with individual patient's data to generate real-time, personalized corrective feedback for improvement. With the dataset of rehabilitation exercises from 15 post-stroke survivors, we demonstrated our system significantly improves its performance to assess patients' exercises while tuning with held-out patient's data. In addition, our real-world evaluation study showed that our system can adapt to new participants and achieved 0.81 average performance to assess their exercises, which is comparable to the experts' agreement level. We further discuss the potential benefits and limitations of our system in practice.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9761426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Generating predicate suggestions based on the space of plans: an example of planning with preferences. 基于规划的空间生成谓词建议:一个带有偏好的规划示例。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-022-09327-w

Gerard Canal, Carme Torras, Guillem Alenyà

{"title":"Generating predicate suggestions based on the space of plans: an example of planning with preferences.","authors":"Gerard Canal, Carme Torras, Guillem Alenyà","doi":"10.1007/s11257-022-09327-w","DOIUrl":"https://doi.org/10.1007/s11257-022-09327-w","url":null,"abstract":"Task planning in human-robot environments tends to be particularly complex as it involves additional uncertainty introduced by the human user. Several plans, entailing few or various differences, can be obtained to solve the same given task. To choose among them, the usual least-cost plan criteria is not necessarily the best option, because here, human constraints and preferences come into play. Knowing these user preferences is very valuable to select an appropriate plan, but the preference values are usually hard to obtain. In this context, we propose the Space-of-Plans-based Suggestions (SoPS) algorithms that can provide suggestions for some planning predicates, which are used to define the state of the environment in a task planning problem where actions modify the predicates. We denote these predicates as suggestible predicates, of which user preferences are a particular case. The first algorithm is able to analyze the potential effect of the unknown predicates and provide suggestions to values for these unknown predicates that may produce better plans. The second algorithm is able to suggest changes to already known values that potentially improve the obtained reward. The proposed approach utilizes a Space of Plans Tree structure to represent a subset of the space of plans. The tree is traversed to find the predicates and the values that would most increase the reward, and output them as a suggestion to the user. Our evaluation in three preference-based assistive robotics domains shows how the proposed algorithms can improve task performance by suggesting the most effective predicate values first.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9752798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Automatically detecting task-unrelated thoughts during conversations using keystroke analysis. 自动检测任务无关的想法在对话中使用击键分析。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-022-09340-z

Vishal Kuvar, Nathaniel Blanchard, Alexander Colby, Laura Allen, Caitlin Mills

引用次数: 3

Using latent variable models to make gaming-the-system detection robust to context variations. 使用潜在变量模型使博弈系统检测对上下文变化具有鲁棒性。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 Epub Date: 2023-05-18 DOI: 10.1007/s11257-023-09362-1

Yun Huang, Steven Dang, J Elizabeth Richey, Pallavi Chhabra, Danielle R Thomas, Michael W Asher, Nikki G Lobczowski, Elizabeth A McLaughlin, Judith M Harackiewicz, Vincent Aleven, Kenneth R Koedinger

{"title":"Using latent variable models to make gaming-the-system detection robust to context variations.","authors":"Yun Huang, Steven Dang, J Elizabeth Richey, Pallavi Chhabra, Danielle R Thomas, Michael W Asher, Nikki G Lobczowski, Elizabeth A McLaughlin, Judith M Harackiewicz, Vincent Aleven, Kenneth R Koedinger","doi":"10.1007/s11257-023-09362-1","DOIUrl":"10.1007/s11257-023-09362-1","url":null,"abstract":"Gaming the system, a behavior in which learners exploit a system's properties to make progress while avoiding learning, has frequently been shown to be associated with lower learning. However, when we applied a previously validated gaming detector across conditions in experiments with an algebra tutor, the detected gaming was not associated with reduced learning, challenging its validity in our study context. Our exploratory data analysis suggested that varying contextual factors across and within conditions contributed to this lack of association. We present a new approach, latent variable-based gaming detection (LV-GD), that controls for contextual factors and more robustly estimates student-level latent gaming tendencies. In LV-GD, a student is estimated as having a high gaming tendency if the student is detected to game more than the expected level of the population given the context. LV-GD applies a statistical model on top of an existing action-level gaming detector developed based on a typical human labeling process, without additional labeling effort. Across three datasets, we find that LV-GD consistently outperformed the original detector in validity measured by association between gaming and learning as well as reliability. LV-GD also afforded high practical utility: it more accurately revealed intervention effects on gaming, revealed a correlation between gaming and perceived competence in math and helped understand productive detected gaming behaviors. Our approach is not only useful for others wanting a cost-effective way to adapt a gaming detector to their context but is also generally applicable in creating robust behavioral measures.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41217734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations. 介绍CARESSER:现场学习机器人社会援助的框架，由专家知识和演示。

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2023-01-01 DOI: 10.1007/s11257-021-09316-5

Antonio Andriella, Carme Torras, Carla Abdelnour, Guillem Alenyà

{"title":"Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations.","authors":"Antonio Andriella, Carme Torras, Carla Abdelnour, Guillem Alenyà","doi":"10.1007/s11257-021-09316-5","DOIUrl":"https://doi.org/10.1007/s11257-021-09316-5","url":null,"abstract":"Socially assistive robots have the potential to augment and enhance therapist's effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots' behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist's expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (N = 22) were requested to solve cognitive exercises with the support of a therapist and later on of a robot endowed with CARESSER. Results showed that: (i) the robot managed to keep the patients' performance stable during the sessions even more so than the therapist; (ii) the assistance offered by the robot during the sessions eventually matched the therapist's preferences. We conclude that CARESSER, with its stakeholder-centric design, can pave the way to new AI approaches that learn by leveraging human-human interactions along with human expertise, which has the benefits of speeding up the learning process, eliminating the need for the design of complex reward functions, and finally avoiding undesired states.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8916953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9365606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12