[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124575-en":3,"doc-seo-124575-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124575,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Enhancing sentient embodied conversational agents with machine learning","Within intelligent user interfaces, the work proposes Sentient Embodied Conversational Agents (SECAs), virtual characters designed for complex dialogue and endowed with human-like sentient capabilities. The paper presents SECA architecture and a publicly available software library to support proactive, sensitive agent behaviors in applications such as education and elder-care. Evaluation compares a basic text-processing version and a machine-learning enhanced understanding model, showing improved perceived understanding: response error rate drops from 22.31% to 11.46%, and 99.33% of participants rate the virtual tutor experience as satisfactory.","Dear Author  \nPlease use this PDF proof to check the layout of your article. If you would like any changes tobe made to the layout, you can leave instructions in the online proofing interface. Making your changes directly in the online proofing interface is the quickest, easiest way to correct and submit your proof. Please note that changes made to the article in the online proofing interface will be added to the article before publication, but are not reflected in this PDF proof.  \nIf you would prefer to submit your corrections by annotating the PDF proof, please download and submit an annotatable PDF proof by clicking here and you'll be redirected to our PDF Proofing system.  \n ARTICLE IN PRESS   \nJID: PATREC [m5G;November 26, 2019;15:22]  \nPattern Recognition Letters xxx (xxxx) xxx  \nEnhancing sentient embodied conversational agents with machine learning  \nQ1 Dolça Tellolsa,∗, Maite Lopez-Sanchez b, Inmaculada Rodríguez b, Pablo Almajanob, Anna Puig b  \na School of Computing, Tokyo Institute of Technology, Meguro Ôokayama 2-12-1, Tokyo 152-8550, Japan  \nb University of Barcelona, Gran Via de les Corts Catalanes, 585, Barcelona 08007, Spain  \n\n| a r t i c l e i n f o |  | a b s t r a c t |\n| --- | --- | --- |\n| Article history:\u003Cbr>Received 5 July 2019\u003Cbr>Revised 11 November 2019\u003Cbr>Accepted 25 November 2019\u003Cbr>Available online xxx |  | Within the area of intelligent User Interfaces, we propose what we call Sentient Embodied Conversational Agents (SECAs): virtual characters able to engage users in complex conversations and to incorporate sentient capabilities similar to the ones humans have. This paper introduces SECAs together with their architecture and a publicly available software library that facilitates their inclusion in applications –such as educational and elder-care– requiring proactive and sensitive agent behaviours. In fact, we illustrate our proposal with a virtual tutor embedded in an educational application for children. The evaluation was performed in two stages: firstly, we tested a version with basic textual processing capabilities; and secondly, we evaluated a SECA with Machine-Learning-enhanced user understanding capabilities. The results show a significant improvement in users’ perception of the agent’s understanding capability. Indeed, the Response Error Rate decreased from 22.31% to 11.46% using ML techniques. Moreover, 99.33% of the participants consider the global experience of talking with the virtual tutor with sentient capabilities tobe satisfactory.\u003Cbr>© 2019 Published by Elsevier B.V. |\n| MSC:\u003Cbr>41A05\u003Cbr>41A10\u003Cbr>65D05\u003Cbr>65D17\u003Cbr>Keywords:\u003Cbr>Embodied conversational agents\u003Cbr>Machine learning\u003Cbr>Virtual tutors |  |  |\n\n1 1. Introduction  \n2 The current surge in the popularity of chatbots has led to a pro- 3 liferation of platforms that facilitate their design and implementa- 4 tion. Chatbots are non-embodied agents designed for communicat- 5 ing with the user by means of simple conversational interactions.  \n6 However, although chatbots can be useful, they fall short when  \n7 aiming to engage the user in longer and more diverse and com- 8 plex conversations.  \n9 The embodiment of Virtual agents, such as Embodied Conversa- 10 tional Agents (ECAs) [5], represents an improvement in user-agent  \n11 interaction, not only because agent personification facilitates verbal  \n12 communication, but also because it allows for enriched interaction  \n13 incorporating non-verbal communication. ECAs are therefore useful  \n14 for training, guiding, and giving support to users in a more natural  \n15 way through the use of both natural language and body language.  \n16 However, ECAs are usually created ad hoc for a specific purpose, 17 hindering their subsequent reuse and the evolution of their func- 18 tional and structural components.  \n∗ Corresponding author.  \nE-mail address: [tellols.d.aa@m.titech.ac.jp](tellols.d.aa@m.titech.ac.jp) (D. Tellols).  \nAgainst this background, we go a step further in the state of 19 ","cbCaikxNqE0WzUCw","https://ap.wps.com/l/cbCaikxNqE0WzUCw","pdf",1235797,1,"English","en",105,"# Introduction\n## Background: chatbots and embodied conversational agents\n## Proposed approach: Sentient Embodied Conversational Agents (SECAs)\n## Architecture and software library\n## Evaluation approach and results","[{\"question\":\"What are Sentient Embodied Conversational Agents (SECAs)?\",\"answer\":\"SECAs are virtual embodied characters that engage users in complex conversations while incorporating sentient capabilities such as personality, needs, and empathy.\"},{\"question\":\"How does machine learning improve SECA understanding?\",\"answer\":\"Machine-learning-enhanced user understanding improves how SECAs interpret users’ inputs, leading to measurable reductions in response error rate.\"},{\"question\":\"What do the evaluation results show?\",\"answer\":\"Across two testing stages, users perceived stronger understanding with ML techniques, reducing response error rate from 22.31% to 11.46%, and 99.33% of participants found the virtual tutor experience satisfactory.\"}]","Enhancing sentient embodied conversational agents with machine learning | 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