Literature DB >> 34960538

Conversational Agents: Goals, Technologies, Vision and Challenges.

Merav Allouch1, Amos Azaria1, Rina Azoulay2.   

Abstract

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

Entities:  

Keywords:  conversational agents; human–agent interaction; smart environments

Mesh:

Year:  2021        PMID: 34960538      PMCID: PMC8704682          DOI: 10.3390/s21248448

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  18 in total

1.  Deep Reinforcement Learning for Sequence-to-Sequence Models.

Authors:  Yaser Keneshloo; Tian Shi; Naren Ramakrishnan; Chandan K Reddy
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2019-08-15       Impact factor: 10.451

2.  Use of an interactive computer agent to support breastfeeding.

Authors:  Roger A Edwards; Timothy Bickmore; Lucia Jenkins; Mary Foley; Justin Manjourides
Journal:  Matern Child Health J       Date:  2013-12

3.  Assistive Conversational Agent for Health Coaching: A Validation Study.

Authors:  Ahmed Fadhil; Yunlong Wang; Harald Reiterer
Journal:  Methods Inf Med       Date:  2019-05-22       Impact factor: 2.176

4.  Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants.

Authors:  Matthew B Hoy
Journal:  Med Ref Serv Q       Date:  2018 Jan-Mar

5.  Improving social skills in children with ASD using a long-term, in-home social robot.

Authors:  Brian Scassellati; Laura Boccanfuso; Chien-Ming Huang; Marilena Mademtzi; Meiying Qin; Nicole Salomons; Pamela Ventola; Frederick Shic
Journal:  Sci Robot       Date:  2018-08-22

Review 6.  Self-Guided Web-Based Interventions: Scoping Review on User Needs and the Potential of Embodied Conversational Agents to Address Them.

Authors:  Mark R Scholten; Saskia M Kelders; Julia Ewc Van Gemert-Pijnen
Journal:  J Med Internet Res       Date:  2017-11-16       Impact factor: 5.428

7.  A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing.

Authors:  A-Yeong Kim; Hyun-Je Song; Seong-Bae Park
Journal:  Comput Intell Neurosci       Date:  2018-10-18

8.  Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management.

Authors:  Mark A Neerincx; Willeke van Vught; Olivier Blanson Henkemans; Elettra Oleari; Joost Broekens; Rifca Peters; Frank Kaptein; Yiannis Demiris; Bernd Kiefer; Diego Fumagalli; Bert Bierman
Journal:  Front Robot AI       Date:  2019-11-15

9.  Conversational agents in healthcare: a systematic review.

Authors:  Liliana Laranjo; Adam G Dunn; Huong Ly Tong; Ahmet Baki Kocaballi; Jessica Chen; Rabia Bashir; Didi Surian; Blanca Gallego; Farah Magrabi; Annie Y S Lau; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2018-09-01       Impact factor: 4.497

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