Статті в журналах: "Assistant conversationnel"

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xuetao, Mao, Jean-Paul Sansonnet, and François Bouchet. "Définition d’un agent conversationnel assistant d’applications internet à partir d’un corpus de requêtes." Techniques et sciences informatiques 29, no. 10 (December 30, 2010): 1123–54. http://dx.doi.org/10.3166/tsi.29.1123-1154.

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Bickmore, Timothy W., Stefán Ólafsson, and Teresa K. O'Leary. "Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment." Journal of Medical Internet Research 23, no. 11 (November 9, 2021): e30704. http://dx.doi.org/10.2196/30704.

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Background Prior studies have demonstrated the safety risks when patients and consumers use conversational assistants such as Apple’s Siri and Amazon’s Alexa for obtaining medical information. Objective The aim of this study is to evaluate two approaches to reducing the likelihood that patients or consumers will act on the potentially harmful medical information they receive from conversational assistants. Methods Participants were given medical problems to pose to conversational assistants that had been previously demonstrated to result in potentially harmful recommendations. Each conversational assistant’s response was randomly varied to include either a correct or incorrect paraphrase of the query or a disclaimer message—or not—telling the participants that they should not act on the advice without first talking to a physician. The participants were then asked what actions they would take based on their interaction, along with the likelihood of taking the action. The reported actions were recorded and analyzed, and the participants were interviewed at the end of each interaction. Results A total of 32 participants completed the study, each interacting with 4 conversational assistants. The participants were on average aged 42.44 (SD 14.08) years, 53% (17/32) were women, and 66% (21/32) were college educated. Those participants who heard a correct paraphrase of their query were significantly more likely to state that they would follow the medical advice provided by the conversational assistant (χ21=3.1; P=.04). Those participants who heard a disclaimer message were significantly more likely to say that they would contact a physician or health professional before acting on the medical advice received (χ21=43.5; P=.001). Conclusions Designers of conversational systems should consider incorporating both disclaimers and feedback on query understanding in response to user queries for medical advice. Unconstrained natural language input should not be used in systems designed specifically to provide medical advice.

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L, Manigandan, and Sivakumar Alur. "Mapping the Research Landscape of Chatbots, Conversational Agents, and Virtual Assistants in Business, Management, and Accounting: A Bibliometric Review." Qubahan Academic Journal 3, no. 4 (December 15, 2023): 502–13. http://dx.doi.org/10.58429/qaj.v3n4a252.

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This bibliometric review aims to map the research landscape of chatbots, conversational agents, and virtual assistants in the business, management, and accounting of the subject area. The review begins by examining the growth of research in this area over time, revealing an increasing interest in the application of chatbots, conversational agents, and virtual assistants in business, management, and accounting contexts. This study aims to contribute to the field of chatbot research by conducting a bibliometric analysis of chatbot, conversational agent and virtual assistant papers available in the Scopus databases. A comprehensive analysis of 378 articles was performed utilizing the Bibliometrix software package. The keywords "Chatbot", "conversational agent" and “virtual assistant” emerged as the most frequently employed terms in the majority of publications. It explores the various domains within which these technologies have been studied, including customer service, marketing, human resources, and financial management.

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Ansari, Tarique. "Conversational AI Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1169–72. http://dx.doi.org/10.22214/ijraset.2022.47554.

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Abstract: A conversational assistant is an intelligent conversational computing system designed to mimic human speech to provide automated online guidance and support. The growing benefits of conversational support have led to widespread adoption across many industries to provide virtual support to their customers. Conversation assistance uses methods and algorithms from his two fields of artificial intelligence: natural language processing and machine learning. However, the application has many challenges and limitations. This research reviews recent advances in conversation support using artificial intelligence and natural language processing. We highlight the main challenges and limitations of the current work and provide recommendations for future research investigations.

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Geetha, Dr V., Dr C. K. Gomathy*, Mr Kottamasu Manasa Sri Vardhan, and Mr Nukala Pavan Kumar. "The Voice Enabled Personal Assistant for Pc using Python." International Journal of Engineering and Advanced Technology 10, no. 4 (April 30, 2021): 162–65. http://dx.doi.org/10.35940/ijeat.d2425.0410421.

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Personal Assistants, or conversational interfaces, or chat bots reinvent a new way for individuals to interact with computes. A Personal Virtual Assistant allows a user to simply ask questions in the same manner that they would address a human, and are even capable of doing some basic tasks like opening apps, reading out news, taking notes etc., with just a voice command. Personal Assistants like Google Assistant, Alexa, Siri wo

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Khandave, Vrushali. "HOMMIE: CONVERSATIONAL AI ASSISTANT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 14, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30718.

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Conversational AI assistants have witnessed remarkable progress in recent years, transforming human-computer interaction across a spectrum of applications. This paper offers a comprehensive overview of the state-of-the-art techniques, methodologies, and challenges in the field. We examine the core components of conversational AI, including natural language understanding, dialogue management, and response generation. Additionally, we address key challenges such as context modeling, personalization, and ethical considerations. The paper serves as a roadmap for researchers and developers, highlighting current achievements and avenues for future advancements in the dynamic domain of conversational AI. keyword - Conversational AI, NLP, Assistant, Machine Learning, Chatbot

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Minh, Nguyen Thi Hong, and Le Duy Khanh. "Using Artificial Intelligence Tools for Developing Conversational Skills for English Majors." European Modern Studies Journal 8, no. 2 (May 30, 2024): 461–69. http://dx.doi.org/10.59573/emsj.8(2).2024.40.

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Despite the potential of Virtual Conversation Assistants as effective tools for conversing support in developing students’ conversational skills, their presence in Vietnamese schools or universities remains limited, often due to cost constraints, quality of language interaction, and their unpopularity among Vietnamese students. Therefore, it is essential that English language educators seek alternative strategies to offer additional assistance for the enhancement of students' abilities to converse effectively. This experimental study pilots the implementation of Call Annie and Praktika, two artificial intelligence (AI) technologies in a local teacher education university in Vietnam, as an innovative approach to the fostering of students' conversational skills. The research discusses the utilization of two such virtual conversational assistant tools for a group of 14 English major freshmen as the experimental group, examining their impacts on the development of students’ conversational proficiency compared to another 14 students in the control group. The study findings analyzed indicate that there was a remarkable improvement in the experimental group’s conversational skills regarding their initiation of conversation, and their active listening through two AI tools. Moreover, there was also remarkable improvement in their verbal fluency and clarity; which contributed to their adaptability, flexibility, and conversational engagement. Moreover, it suggests further application of AI tools in educational contexts to facilitate the instruction and reinforcement of students' conversational skills, where human expertise seamlessly integrates with the power of AI tools to create a more effectively expanded language learning environment.

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Ortiz, Charles L. "Holistic Conversational Assistants." AI Magazine 39, no. 1 (March 27, 2018): 88–90. http://dx.doi.org/10.1609/aimag.v39i1.2771.

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This column describes work being done at Nuance Communication in developing virtual personal assistants (VPAs) that can engage in extended task center dialogues and the involve the coordination of many complex modules, along with conversational and collaborative support to such VPAs.

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Pappula, Sharmila Reddy, and Sathwik Rao Allam. "LLMs for Conversational AI: Enhancing Chatbots and Virtual Assistants." International Journal of Research Publication and Reviews 4, no. 12 (December 9, 2023): 1601–11. http://dx.doi.org/10.55248/gengpi.4.1223.123425.

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Parikh, Soham, Quaizar Vohra, and Mitul Tiwari. "Automated Utterance Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (April 3, 2020): 13344–49. http://dx.doi.org/10.1609/aaai.v34i08.7047.

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Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need for an automated utterance generation. In this paper, we propose an utterance generation system which 1) uses extractive summarization to extract important sentences from the description, 2) uses multiple paraphrasing techniques to generate a diverse set of paraphrases of the title and summary sentences, and 3) selects good candidate paraphrases with the help of a novel candidate selection algorithm.

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Raees, Shaikh Areeba. "CHAT GPT ASSISTANT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 7, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30218.

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The capacity of ChatGPT, a cutting-edge conversational AI model, to produce text responses that resemble those of a human being in a range of conversational contexts has attracted a lot of attention. By using extensive pretraining on a variety of textual data, ChatGPT generates discourse with impressive fluency and coherence, opening up new possibilities for improving human-computer interaction. In this study, we give a thorough analysis of ChatGPT's capabilities and constraints, examining how well it generates interesting and natural-sounding discussions. We explore several facets of ChatGPT's dialogue production through empirical evaluation and qualitative analysis, such as its flexibility across domains, responsiveness to input cues, and propensity to produce a range of contextually relevant and varied responses. Additionally, we look at methods for adjusting ChatGPT to certain conversational tasks and evaluate how different training regimens affect its Index Terms Data Collection and Preprocessing, Machine Learning Models based, Prediction and Visualization, Real-time Update.

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Seymour, William, and Max Van Kleek. "Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (October 13, 2021): 1–16. http://dx.doi.org/10.1145/3479515.

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Modern conversational agents such as Alexa and Google Assistant represent significant progress in speech recognition, natural language processing, and speech synthesis. But as these agents have grown more realistic, concerns have been raised over how their social nature might unconsciously shape our interactions with them. Through a survey of 500 voice assistant users, we explore whether users' relationships with their voice assistants can be quantified using the same metrics as social, interpersonal relationships; as well as if this correlates with how much they trust their devices and the extent to which they anthropomorphise them. Using Knapp's staircase model of human relationships, we find that not only can human-device interactions be modelled in this way, but also that relationship development with voice assistants correlates with increased trust and anthropomorphism.

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Huang, Ting-Hao, Walter Lasecki, Amos Azaria, and Jeffrey Bigham. ""Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 79–88. http://dx.doi.org/10.1609/hcomp.v4i1.13292.

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Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly converses with end users over time by recruiting workers on demand, which in turn decide what might be the best response for each user sentence. Up to the first month of our deployment, 59 users have held conversations with Chorus during 320 conversational sessions. In this paper, we present an account of Chorus' deployment, with a focus on four challenges: (i) identifying when conversations are over, (ii) malicious users and workers, (iii) on-demand recruiting, and (iv) settings in which consensus is not enough. Our observations could assist the deployment of crowd-powered conversation systems and crowd-powered systems in general.

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Krommyda, Maria, and Verena Kantere. "Semantic Analysis for Conversational Datasets: Improving Their Quality Using Semantic Relationships." International Journal of Semantic Computing 14, no. 03 (September 2020): 395–422. http://dx.doi.org/10.1142/s1793351x2050004x.

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As more and more datasets become available, their utilization in different applications increases in popularity. Their volume and production rate, however, means that their quality and content control is in most cases non-existing, resulting in many datasets that contain inaccurate information of low quality. Especially, in the field of conversational assistants, where the datasets come from many heterogeneous sources with no quality assurance, the problem is aggravated. We present here an integrated platform that creates task- and topic-specific conversational datasets to be used for training conversational agents. The platform explores available conversational datasets, extracts information based on semantic similarity and relatedness, and applies a weight-based score function to rank the information based on its value for the specific task and topic. The finalized dataset can then be used for the training of an automated conversational assistance over accurate data of high quality.

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Acer, Utku Günay, Marc van den Broeck, Chulhong Min, Mallesham Dasari, and Fahim Kawsar. "The City as a Personal Assistant." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (July 4, 2022): 1–31. http://dx.doi.org/10.1145/3534573.

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Conversational agents are increasingly becoming digital partners in our everyday computational experiences. Although rich and fresh in content, they are oblivious to users' locality beyond geospatial weather and traffic conditions. We introduce Lingo, a hyper-local conversational agent embedded deeply into the urban infrastructure that provides rich, purposeful, detailed, and in some cases, playful information relevant to a neighbourhood. Drawing lessons from a mixed-method contextual study (online survey, n = 1992 and semi-structured interviews, n = 21), we identify requirements for such a hyper-local conversational agent and a sample set of questions serving urban neighbourhoods of Belgium. Our agent design is manifested into a two-part system. First, a multi-modal reasoning engine serves as a hyper-local information source using automated machine-learning models operating on camera, microphone, and environmental sensor data. Second, a smart conversational speaker and a smartphone application serve as hyper-local information access points. Finally, we introduce a covert communication mechanism over Wi-Fi management frames that bridges the two parts of our Lingo system and enables the privacy-preserving proxemic interactions. We describe the design, implementation, and technical assessment of Lingo together with usability (n = 20) and real-world deployment (n = 5) studies. We reflect on information quality, accessibility benefits, and interaction dynamics and demonstrate the efficacy of Lingo in offering hyper-local information at the finest granularity in urban neighbourhoods while reducing access time up to a factor of 25.

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Bajaj, Divij, and Dhanya Pramod. "Conversational System, Intelligent Virtual Assistant (IVA) Named DIVA Using Raspberry Pi." International Journal of Security and Privacy in Pervasive Computing 12, no. 4 (October 2020): 38–52. http://dx.doi.org/10.4018/ijsppc.2020100104.

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Humans are living in an era where they are interacting with machines day in and day out. In this new era of the 21st century, a virtual assistant (IVA) is a boon for everyone. It has opened the way for a new world where devices can interact their own. The human voice is integrated with every device making it intelligent. These IVAs can also be used to integrate it with business intelligence software such as Tableau and PowerBI to give dashboards the power of voice and text insights using NLG (natural language generation). This new technology attracted almost the entire world like smart phones, laptops, computers, smart meeting rooms, car InfoTech system, TV, etc. in many ways. Some of the popular voice assistants are like Mibot, Siri, Google Assistant, Cortana, Bixby, and Amazon Alexa. Voice recognition, contextual understanding, and human interaction are some of the issues that are continuously improving in these IVAs and shifting this paradigm towards AI research. This research aims at processing human natural voice and gives a meaningful response to the user. The questions that it is not able to answer are stored in a database for further investigation.

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Cebrián, Javier, Ramón Martínez-Jiménez, Natalia Rodriguez, and Luis Fernando D’Haro. "Considerations on Creating Conversational Agents For Multiple Environments and Users." AI Magazine 42, no. 2 (October 20, 2021): 71–86. http://dx.doi.org/10.1609/aimag.v42i2.7484.

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Advances in artificial intelligence algorithms and expansion of straightforward cloud-based platforms have enabled the adoption of conversational assistants by both, medium and large companies, to facilitate interaction between clients and employees. The interactions are possible through the use of ubiquitous devices (e.g., Amazon Echo, Apple HomePod, Google Nest), virtual assistants (e.g., Apple Siri, Google Assistant, Samsung Bixby, or Microsoft Cortana), chat windows on the corporate website, or social network applications (e.g. Facebook Messenger, Telegram, Slack, WeChat).Creating a useful, personalized conversational agent that is also robust and popular is nonetheless challenging work. It requires picking the right algorithm, framework, and/or communication channel, but perhaps more importantly, consideration of the specific task, user needs, environment, available training data, budget, and a thoughtful design.In this paper, we will consider the elements necessary to create a conversational agent for different types of users, environments, and tasks. The elements will account for the limited amount of data available for specific tasks within a company and for non-English languages. We are confident that we can provide a useful resource for the new practitioner developing an agent. We can point out novice problems/traps to avoid, create consciousness that the development of the technology is achievable despite comprehensive and significant challenges, and raise awareness about different ethical issues that may be associated with this technology. We have compiled our experience with deploying conversational systems for daily use in multicultural, multilingual, and intergenerational settings. Additionally, we will give insight on how to scale the proposed solutions.

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Pérez, Anxo, Paula Lopez-Otero, and Javier Parapar. "Designing an Open Source Virtual Assistant." Proceedings 54, no. 1 (August 21, 2020): 30. http://dx.doi.org/10.3390/proceedings2020054030.

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A chatbot is a type of agent that allows people to interact with an information repository using natural language. Nowadays, chatbots have been incorporated in the form of conversational assistants on the most important mobile and desktop platforms. In this article, we present our design of an assistant developed with open-source and widely used components. Our proposal covers the process end-to-end, from information gathering and processing to visual and speech-based interaction. We have deployed a proof of concept over the website of our Computer Science Faculty.

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Brinkschulte, Luisa, Stephan Schlögl, Alexander Monz, Pascal Schöttle, and Matthias Janetschek. "Perspectives on Socially Intelligent Conversational Agents." Multimodal Technologies and Interaction 6, no. 8 (July 25, 2022): 62. http://dx.doi.org/10.3390/mti6080062.

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The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to the exploration of relevant traits and their potential embedding in future agent technology. Aiming to lay a foundation for further developments, we report on the results of a Delphi study highlighting the respective opinions of 21 multi-disciplinary domain experts. Results exhibit 14 distinctive characteristics of social intelligence, grouped into different levels of consensus, maturity, and abstraction, which may be considered a relevant basis, assisting the definition and consequent development of socially intelligent conversational agents.

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Shrivastava, Pragya, and G. Bharadwaja Kumar. "WORKING OF ACONTEXT-AWARE CONVERSATIONAL ENTITY." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 202. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19638.

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Abstract — Introduction of new technologies in to the world is increasing rapidly and in order to assist the users to get equipped with such technologies industries are providing customer care services. Contacting a customer care service is subjective to several overheads of selecting options from a listed set, waiting for the switching between selections and awaiting the support of a customer care executive as the process usually requires a human intervention. Hence, a substitute for a personnel is required by the IT industries in order to automate the communication process in assisting the customers. Chatbots with context aware question-answering capabilities can be viewed as a good solution to such customer-care assistance. Development of a chatbot and the complexities involved in getting it to work effectively is delineated in this paper.

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Davis, Courtney R., Karen J. Murphy, Rachel G. Curtis, and Carol A. Maher. "A Process Evaluation Examining the Performance, Adherence, and Acceptability of a Physical Activity and Diet Artificial Intelligence Virtual Health Assistant." International Journal of Environmental Research and Public Health 17, no. 23 (December 7, 2020): 9137. http://dx.doi.org/10.3390/ijerph17239137.

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Artificial intelligence virtual health assistants are a promising emerging technology. This study is a process evaluation of a 12-week pilot physical activity and diet program delivered by virtual assistant “Paola”. This single-arm repeated measures study (n = 28, aged 45–75 years) was evaluated on technical performance (accuracy of conversational exchanges), engagement (number of weekly check-ins completed), adherence (percentage of step goal and recommended food servings), and user feedback. Paola correctly asked scripted questions and responded to participants during the check-ins 97% and 96% of the time, respectively, but correctly responded to spontaneous exchanges only 21% of the time. Participants completed 63% of weekly check-ins and conducted a total of 3648 exchanges. Mean dietary adherence was 91% and was lowest for discretionary foods, grains, red meat, and vegetables. Participants met their step goal 59% of the time. Participants enjoyed the program and found Paola useful during check-ins but not for spontaneous exchanges. More in-depth knowledge, personalized advice and spontaneity were identified as important improvements. Virtual health assistants should ensure an adequate knowledge base and ability to recognize intents and entities, include personality and spontaneity, and provide ongoing technical troubleshooting of the virtual assistant to ensure the assistant remains effective.

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Bouchet, François, and Jean-Paul Sansonnet. "Agents conversationnels psychologiques. Modélisation des réactions rationnelles et comportementales des agents assistants conversationnels." Revue d'intelligence artificielle 27, no. 6 (December 30, 2013): 679–708. http://dx.doi.org/10.3166/ria.27.679-708.

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Rastogi, Abhinav, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. "Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8689–96. http://dx.doi.org/10.1609/aaai.v34i05.6394.

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Virtual assistants such as Google Assistant, Alexa and Siri provide a conversational interface to a large number of services and APIs spanning multiple domains. Such systems need to support an ever-increasing number of services with possibly overlapping functionality. Furthermore, some of these services have little to no training data available. Existing public datasets for task-oriented dialogue do not sufficiently capture these challenges since they cover few domains and assume a single static ontology per domain. In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains. Our dataset exceeds the existing task-oriented dialogue corpora in scale, while also highlighting the challenges associated with building large-scale virtual assistants. It provides a challenging testbed for a number of tasks including language understanding, slot filling, dialogue state tracking and response generation. Along the same lines, we present a schema-guided paradigm for task-oriented dialogue, in which predictions are made over a dynamic set of intents and slots, provided as input, using their natural language descriptions. This allows a single dialogue system to easily support a large number of services and facilitates simple integration of new services without requiring additional training data. Building upon the proposed paradigm, we release a model for dialogue state tracking capable of zero-shot generalization to new APIs, while remaining competitive in the regular setting.

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Hwang, Inseok, Youngki Lee, Chungkuk Yoo, Chulhong Min, Dongsun Yim, and John Kim. "Towards Interpersonal Assistants: Next-Generation Conversational Agents." IEEE Pervasive Computing 18, no. 2 (April 1, 2019): 21–31. http://dx.doi.org/10.1109/mprv.2019.2922907.

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Mohammed, Dr Farheen. "Conversational AI: Designing Intelligent Chatbots and Virtual Assistants." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 28, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34774.

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Conversational AI has revolutionized the way humans interact with machines. By leveraging advances in Natural Language Processing (NLP) and Machine Learning (ML), intelligent Chatbots and virtual assistants have become integral tools across various sectors. This paper explores the design principles, technological frameworks, and application areas of conversational AI, focusing on the challenges and future directions of this rapidly evolving field.

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Mekni, Mehdi. "An Artificial Intelligence Based Virtual Assistant Using Conversational Agents." Journal of Software Engineering and Applications 14, no. 09 (2021): 455–73. http://dx.doi.org/10.4236/jsea.2021.149027.

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Kim, Kang-Min, Woo-Jong Ryu, Jun-Hyung Park, and SangKeun Lee. "meChat: In-Device Personal Assistant for Conversational Photo Sharing." IEEE Internet Computing 23, no. 2 (March 1, 2019): 23–30. http://dx.doi.org/10.1109/mic.2018.2883059.

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Bouchet, François, and Jean-Paul Sansonnet. "Agents conversationnels psychologiques. Un cadre d'étude des comportements rationnels et psychologiques des agents assistants conversationnels." Revue d'intelligence artificielle 25, no. 5 (October 30, 2011): 591–623. http://dx.doi.org/10.3166/ria.25.591-623.

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Fontecha, Jesús, Iván González, and Alberto Salas-Seguín. "Using Conversational Assistants and Connected Devices to Promote a Responsible Energy Consumption at Home." Proceedings 31, no. 1 (November 20, 2019): 32. http://dx.doi.org/10.3390/proceedings2019031032.

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Today, households worldwide are being increasingly connected. Mobile devices and embedded systems carry out many tasks supported by applications which are based on artificial intelligence algorithms with the aim of leading homes to be smarter. One of the purposes of these systems is to connect appliances to the power network, as well as to the internet to monitor consumption data among others. In addition, new interaction ways are emerging to manage all these systems. For example, conversational assistants which allow us to interact by voice with devices at home. In this work, we present GreenMoCA, a system to monitor energy consumption data from connected devices at home with the aim of improving sustainability aspects and reducing such energy consumption, supported by a conversational assistant. This system is able to interact with the user in a natural way, providing information of current energy use and feedback based on previous consumption measures in a Smart Home environment. Finally, we assessed GreenMoCA from a usability and user experience approach on a group of users with positive results.

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Curtis, Rachel G., Bethany Bartel, Ty Ferguson, Henry T. Blake, Celine Northcott, Rosa Virgara, and Carol A. Maher. "Improving User Experience of Virtual Health Assistants: Scoping Review." Journal of Medical Internet Research 23, no. 12 (December 21, 2021): e31737. http://dx.doi.org/10.2196/31737.

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Background Virtual assistants can be used to deliver innovative health programs that provide appealing, personalized, and convenient health advice and support at scale and low cost. Design characteristics that influence the look and feel of the virtual assistant, such as visual appearance or language features, may significantly influence users’ experience and engagement with the assistant. Objective This scoping review aims to provide an overview of the experimental research examining how design characteristics of virtual health assistants affect user experience, summarize research findings of experimental research examining how design characteristics of virtual health assistants affect user experience, and provide recommendations for the design of virtual health assistants if sufficient evidence exists. Methods We searched 5 electronic databases (Web of Science, MEDLINE, Embase, PsycINFO, and ACM Digital Library) to identify the studies that used an experimental design to compare the effects of design characteristics between 2 or more versions of an interactive virtual health assistant on user experience among adults. Data were synthesized descriptively. Health domains, design characteristics, and outcomes were categorized, and descriptive statistics were used to summarize the body of research. Results for each study were categorized as positive, negative, or no effect, and a matrix of the design characteristics and outcome categories was constructed to summarize the findings. Results The database searches identified 6879 articles after the removal of duplicates. We included 48 articles representing 45 unique studies in the review. The most common health domains were mental health and physical activity. Studies most commonly examined design characteristics in the categories of visual design or conversational style and relational behavior and assessed outcomes in the categories of personality, satisfaction, relationship, or use intention. Over half of the design characteristics were examined by only 1 study. Results suggest that empathy and relational behavior and self-disclosure are related to more positive user experience. Results also suggest that if a human-like avatar is used, realistic rendering and medical attire may potentially be related to more positive user experience; however, more research is needed to confirm this. Conclusions There is a growing body of scientific evidence examining the impact of virtual health assistants’ design characteristics on user experience. Taken together, data suggest that the look and feel of a virtual health assistant does affect user experience. Virtual health assistants that show empathy, display nonverbal relational behaviors, and disclose personal information about themselves achieve better user experience. At present, the evidence base is broad, and the studies are typically small in scale and highly heterogeneous. Further research, particularly using longitudinal research designs with repeated user interactions, is needed to inform the optimal design of virtual health assistants.

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Kraus, Matthias, Nicolas Wagner, Zoraida Callejas, and Wolfgang Minker. "The Role of Trust in Proactive Conversational Assistants." IEEE Access 9 (2021): 112821–36. http://dx.doi.org/10.1109/access.2021.3103893.

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Bickmore, Timothy W., Ha Trinh, Stefan Olafsson, Teresa K. O'Leary, Reza Asadi, Nathaniel M. Rickles, and Ricardo Cruz. "Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant." Journal of Medical Internet Research 20, no. 9 (September 4, 2018): e11510. http://dx.doi.org/10.2196/11510.

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Agarwal, Vineet, and Anjali Shukla. "Chatbot for Interview." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (July 30, 2022): 46–49. http://dx.doi.org/10.35940/ijrte.b7092.0711222.

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The advent of virtual assistants has made communicating with computers a reality. Chatbots are virtual assistant tools designed to simplify the communication between humans and computers. A chatbot will answer your queries and execute a certain computation if required. Chatbots can be developed using Natural Language Processing (NLP) and Deep Learning. Natural Language Process technique like Naïve bayes can be used. Chatbot can be implemented for a fun purpose like chit-chat; these are called Conversational chatbots. Chatbots designed to answer any questions is known as horizontal chatbots and the specific task-oriented chatbots are known as vertical chatbots (also known as Closed Domain Chatbots). In this paper, we will be discussing a task-oriented chatbot to help recruitment team in the technical round of interview process.

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Gurney, Nikolos, George Loewenstein, and Nick Chater. "Conversational technology and reactions to withheld information." PLOS ONE 19, no. 4 (April 11, 2024): e0301382. http://dx.doi.org/10.1371/journal.pone.0301382.

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People frequently face decisions that require making inferences about withheld information. The advent of large language models coupled with conversational technology, e.g., Alexa, Siri, Cortana, and the Google Assistant, is changing the mode in which people make these inferences. We demonstrate that conversational modes of information provision, relative to traditional digital media, result in more critical responses to withheld information, including: (1) a reduction in evaluations of a product or service for which information is withheld and (2) an increased likelihood of recalling that information was withheld. These effects are robust across multiple conversational modes: a recorded phone conversation, an unfolding chat conversation, and a conversation script. We provide further evidence that these effects hold for conversations with the Google Assistant, a prominent conversational technology. The experimental results point to participants’ intuitions about why the information was withheld as the driver of the effect.

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Frummet, Alexander, David Elsweiler, and Bernd Ludwig. "“What Can I Cook with these Ingredients?” - Understanding Cooking-Related Information Needs in Conversational Search." ACM Transactions on Information Systems 40, no. 4 (October 31, 2022): 1–32. http://dx.doi.org/10.1145/3498330.

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As conversational search becomes more pervasive, it becomes increasingly important to understand the users’ underlying information needs when they converse with such systems in diverse domains. We conduct an in situ study to understand information needs arising in a home cooking context as well as how they are verbally communicated to an assistant. A human experimenter plays this role in our study. Based on the transcriptions of utterances, we derive a detailed hierarchical taxonomy of diverse information needs occurring in this context, which require different levels of assistance to be solved. The taxonomy shows that needs can be communicated through different linguistic means and require different amounts of context to be understood. In a second contribution, we perform classification experiments to determine the feasibility of predicting the type of information need a user has during a dialogue using the turn provided. For this multi-label classification problem, we achieve average F1 measures of 40% using BERT-based models. We demonstrate with examples which types of needs are difficult to predict and show why, concluding that models need to include more context information in order to improve both information need classification and assistance to make such systems usable.

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Dhanda, Armaan, Raman Goel, Sachin Vashisht, and Seba Susan. "Hindi Conversational Agents for Mental Health Assistance." International Journal of Applied Research on Information Technology and Computing 12, no. 1to3 (2021): 12–20. http://dx.doi.org/10.5958/0975-8089.2021.00004.x.

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Bodapati, Mrs Nagaeswari. "Campus Companion : Creating a Supportive Chat – Assistant for Students." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 2, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29939.

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This article explores the creation and usage of chatbots, or intelligent conversational agents, for online communication. Python and other machine learning methods are employed in the chatbot's design. To be more explicit, TensorFlow/Keras is used for natural language processing, MySQL is utilized for database administration, and Flask is used for web hosting. The initiative attempts to accomplish its goals by enhancing the user experience and fostering more effective communication on the website. Using massive datasets, the chatbot will be educated to comprehend users' objectives and offer replies that fit with them. Natural language processing technologies, including voice recognition, natural language interpretation, and natural language production, will be used to foster natural discussions. A number of machine learning approaches, such as transformers, attention processes, and recurrent neural networks, will be investigated in order to classify intent and deliver replies. The objective is to construct a powerful and versatile chatbot that can grasp arguments in their context, preserve the debate's status, and answer in a manner that sounds human. Assessment criteria such as accuracy, recall, and precision will be utilized to develop and adapt the chatbot. Numerous businesses, including education, entertainment, customer service, and other disciplines, may profit from the employment of this chatbot. The implementation of intelligent conversational interfaces on websites has the potential to greatly enhance user experience. Keywords— Chatbot, Flask, MySQL, TensorFlow, Keras, Website Interaction, Natural Language Processing (NLP), Conversational AI, Intent Classification, Response Generation, Machine Learning Models, Recurrent Neural Networks (RNNs), Transformer Models, Attention Mechanisms, User Experience (UX), Contextual Conversations, Dialogue State Management, Human-like Responses, Customer Service Automation, Educational Chatbot, Entertainment Applications.

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Asfoura, Evan, Gamal Kassem, Belal Alhuthaifi, and Fozi Belhaj. "Developing Chatbot Conversational Systems & the Future Generation Enterprise Systems." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 10 (May 22, 2023): 155–75. http://dx.doi.org/10.3991/ijim.v17i10.37851.

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Conversational technology has recently emerged effectively; it helps people in communicating with smart devices such as smartphones by using human language. When they emerged, they enabled and assisted users to perform various functions such as gathering information, conducting transactions, having general conversations, and easily navigating web services and entertainment. They not only have an impact on people in general by improving customer service as they can provide answers to any inquiries but also facilitated navigation by assisting people with disabilities by interacting with a system that deals with voices or any other human language. In addition, recently they started to play a significant role in enterprise systems by supporting employees as they are assisted in learning the newly implemented system. As virtual assistants, they contribute to better accessibility, and acceptance, and also reduce the costs associated with customer service. This paper provides a chatbot system that helps the employees to learn how to deal with the newly installed ERP system flexibly and easily, which helps in solving one of the common problems that occur when switching to the ERP system.

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Almousa, Omar Saad, Hazem Migdady, and Mohammad Al-Talib. "Conversational Frames." International Journal of Embedded and Real-Time Communication Systems 11, no. 4 (October 2020): 104–33. http://dx.doi.org/10.4018/ijertcs.2020100106.

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This paper extends the previous work of Almousa and Migdady by proposing and implementing three models to improve the correctness and naturality of the given answers of smart personal assistants (SPAs). The motivation behind the three proposed models is the failure of some well-known existing SPAs (Siri and Salma) to extract contextual information from a conversation. The authors call this kind of information: the conversational frame. For evaluating the suggested models, the authors also implement an abstraction of the existing SPAs that they call non-framed model. To evaluate the four implementations, 36 respondents answered an on-line questionnaire after watching four videos that the authors recorded per implementation. The authors statistically prove that their three suggested models precede the non-framed model. The researchers attribute this to the fact that our models overcome the shortcoming present in non-framed model. Moreover, their model that implements the conversational frame precedes all other models.

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Agarwal, Sanchit, Jan Jezabek, Arijit Biswas, Emre Barut, Bill Gao, and Tagyoung Chung. "Building Goal-Oriented Dialogue Systems with Situated Visual Context." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13149–51. http://dx.doi.org/10.1609/aaai.v36i11.21710.

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Goal-oriented dialogue agents can comfortably utilize the conversational context and understand its users' goals. However, in visually driven user experiences, these conversational agents are also required to make sense of the screen context in order to provide a proper interactive experience. In this paper, we propose a novel multimodal conversational framework where the dialogue agent's next action and their arguments are derived jointly conditioned both on the conversational and the visual context. We demonstrate the proposed approach via a prototypical furniture shopping experience for a multimodal virtual assistant.

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Nagargoje, Prof Vivek. "J.A.R.V.I.S Organizational Virtual Assistant." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4664–67. http://dx.doi.org/10.22214/ijraset.2021.35066.

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Chatbots, or conversational interfaces as they are also known, present a new way for individuals to interact with computer systems. Traditionally, to get a question answered by a software program involved using a search engine, or filling out a form. A chatbot allows a user to simply ask questions in the same manner that they would address a human. The most well known chatbots currently are voice chatbots: Alexa and Siri. However, chatbots are currently being adopted at a high rate on computer chat platforms. The technology at the core of the rise of the chatbot is natural language processing (“NLP”). A simple chatbot can be created by loading an FAQ (frequently asked questions) into chatbot software. The functionality of the chatbot can be improved by integrating it into the organization’s enterprise software, allowing more personal questions to be answered, like“When is the meet?”, or “What is the schedule of my day?”. A chatbot can be used as an “assistant” to a live agent, increasing the agent’s efficiency.

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Kim, Hyounghun, Doo Soon Kim, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, and Mohit Bansal. "CAISE: Conversational Agent for Image Search and Editing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10903–11. http://dx.doi.org/10.1609/aaai.v36i10.21337.

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Demand for image editing has been increasing as users' desire for expression is also increasing. However, for most users, image editing tools are not easy to use since the tools require certain expertise in photo effects and have complex interfaces. Hence, users might need someone to help edit their images, but having a personal dedicated human assistant for every user is impossible to scale. For that reason, an automated assistant system for image editing is desirable. Additionally, users want more image sources for diverse image editing works, and integrating an image search functionality into the editing tool is a potential remedy for this demand. Thus, we propose a dataset of an automated Conversational Agent for Image Search and Editing (CAISE). To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests. To build such a system, we first collect image search and editing conversations between pairs of annotators. The assistant-annotators are equipped with a customized image search and editing tool to address the requests from the user-annotators. The functions that the assistant-annotators conduct with the tool are recorded as executable commands, allowing the trained system to be useful for real-world application execution. We also introduce a generator-extractor baseline model for this task, which can adaptively select the source of the next token (i.e., from the vocabulary or from textual/visual contexts) for the executable command. This serves as a strong starting point while still leaving a large human-machine performance gap for useful future work. Data and code are available: https://github.com/hyounghk/CAISE.

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Schreuter, Donna, Peter van der Putten, and Maarten H. Lamers. "Trust Me on This One: Conforming to Conversational Assistants." Minds and Machines 31, no. 4 (November 24, 2021): 535–62. http://dx.doi.org/10.1007/s11023-021-09581-8.

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Deshmukh, Prof Anushree, Smit Shah, Heena Puthran, and Naisargi Shah. "Virtual Shopping Assistant for Online Fashion Store." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 110–17. http://dx.doi.org/10.22214/ijraset.2022.42099.

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Abstract: A new way for individuals to interact with computer systems will be done through chatbots or conversational interfaces. Historically, introducing a matter answered by a software package involves employing a program or filling out a type. The technology at the core of the increase of the chatbot is NLP i.e., Natural Language Processing. Sequence to Sequence (often abbreviated to seq2seq) fashions is a specific type of Recurrent Neural Network architectures that we commonly use (but no longer restricted) to clear up complicated Language issues like Machine Translation, Question Answering, growing Chatbots, Text Summarization, and so forth. Recent advances in machine learning have greatly improved the accuracy and effectiveness of NLP, creating chatbots a viable choice for several organizations like e-commerce, Customer service, Conversational apps, social media, Sales/Marketing/Branding, as Voice modules, Travel industry, Medicine, Hospitality, Human Resources etc. An NLP primarily based chatbot is a pc software or synthetic brain that communicates with a patron by means of textual or sound strategies This improvement in NLP is firing a great deal of additional analysis and research which should lead to continued growth in the effectiveness of chatbots in the years to come. Stochastic gradient descent (often abbreviated SGD) is an iterative approach for optimizing an goal characteristic with appropriate smoothness homes (e.g. differentiable or sub differentiable). Usage of Chatbots can also prove to be beneficial in ways like economically offering 24/7 service, improving customer satisfaction, reaching a younger demographic, reducing costs, increasing revenue and much more. Keywords: Chatbots, Natural Language Processing (NLP), Stochastic Gradient Descent (SGD), Sequential Model, Machine Learning

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Lee, Sungjin, and Rahul Jha. "Zero-Shot Adaptive Transfer for Conversational Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6642–49. http://dx.doi.org/10.1609/aaai.v33i01.33016642.

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Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled data is required for training each new domain. While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments. To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments. Extensive experimentation over a dataset of 10 domains relevant to our commercial personal digital assistant shows that our model outperforms previous state-of-the-art systems by a large margin, and achieves an even higher improvement in the low data regime.

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Adaimi, Rebecca, Howard Yong, and Edison Thomaz. "Ok Google, What Am I Doing?" Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (March 19, 2021): 1–24. http://dx.doi.org/10.1145/3448090.

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Conversational assistants in the form of stand-alone devices such as Amazon Echo and Google Home have become popular and embraced by millions of people. By serving as a natural interface to services ranging from home automation to media players, conversational assistants help people perform many tasks with ease, such as setting timers, playing music and managing to-do lists. While these systems offer useful capabilities, they are largely passive and unaware of the human behavioral context in which they are used. In this work, we explore how off-the-shelf conversational assistants can be enhanced with acoustic-based human activity recognition by leveraging the short interval after a voice command is given to the device. Since always-on audio recording can pose privacy concerns, our method is unique in that it does not require capturing and analyzing any audio other than the speech-based interactions between people and their conversational assistants. In particular, we leverage background environmental sounds present in these short duration voice-based interactions to recognize activities of daily living. We conducted a study with 14 participants in 3 different locations in their own homes. We showed that our method can recognize 19 different activities of daily living with average precision of 84.85% and average recall of 85.67% in a leave-one-participant-out performance evaluation with 30-second audio clips bound by the voice interactions.

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Bandlamudi, Jayachandu, Kushal Mukherjee, Prerna Agarwal, Ritwik Chaudhuri, Rakesh Pimplikar, Sampath Dechu, Alex Straley, Anbumunee Ponniah, and Renuka Sindhgatta. "Building Conversational Artifacts to Enable Digital Assistant for APIs and RPAs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 22725–33. http://dx.doi.org/10.1609/aaai.v38i21.30306.

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In the realm of business automation, digital assistants/chatbots are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through APIs and RPAs. To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the "build" phase to assist humans in creating skills for digital assistants. As a result, the system doesn't need to rely on LLMs during conversations with business users, leading to efficient deployment. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.

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de Cock, Caroline, Madison Milne-Ives, Michelle Helena van Velthoven, Abrar Alturkistani, Ching Lam, and Edward Meinert. "Effectiveness of Conversational Agents (Virtual Assistants) in Health Care: Protocol for a Systematic Review." JMIR Research Protocols 9, no. 3 (March 9, 2020): e16934. http://dx.doi.org/10.2196/16934.

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Background Conversational agents (also known as chatbots) have evolved in recent decades to become multimodal, multifunctional platforms with potential to automate a diverse range of health-related activities supporting the general public, patients, and physicians. Multiple studies have reported the development of these agents, and recent systematic reviews have described the scope of use of conversational agents in health care. However, there is scarce research on the effectiveness of these systems; thus, their viability and applicability are unclear. Objective The objective of this systematic review is to assess the effectiveness of conversational agents in health care and to identify limitations, adverse events, and areas for future investigation of these agents. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols will be used to structure this protocol. The focus of the systematic review is guided by a population, intervention, comparator, and outcome framework. A systematic search of the PubMed (Medline), EMBASE, CINAHL, and Web of Science databases will be conducted. Two authors will independently screen the titles and abstracts of the identified references and select studies according to the eligibility criteria. Any discrepancies will then be discussed and resolved. Two reviewers will independently extract and validate data from the included studies into a standardized form and conduct quality appraisal. Results As of January 2020, we have begun a preliminary literature search and piloting of the study selection process. Conclusions This systematic review aims to clarify the effectiveness, limitations, and future applications of conversational agents in health care. Our findings may be useful to inform the future development of conversational agents and promote the personalization of patient care. International Registered Report Identifier (IRRID) PRR1-10.2196/16934

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Lin, Sheng-Chieh, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, and Jimmy Lin. "Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting." ACM Transactions on Information Systems 39, no. 4 (October 31, 2021): 1–29. http://dx.doi.org/10.1145/3446426.

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Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this article, we tackle conversational passage retrieval, an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad hoc IR system. Specifically, we propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, stand-alone, human-understandable queries with a pretrained sequence-to-sequence model. Detailed analyses of the two CQR methods are provided quantitatively and qualitatively, explaining their advantages, disadvantages, and distinct behaviors. Moreover, to leverage the strengths of both CQR methods, we propose combining their output with reciprocal rank fusion, yielding state-of-the-art retrieval effectiveness, 30% improvement in terms of NDCG@3 compared to the best submission of Text REtrieval Conference (TREC) Conversational Assistant Track (CAsT) 2019.

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Li, Chang, and Hideyoshi Yanagisawa. "Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions." PLOS ONE 16, no. 4 (April 23, 2021): e0250326. http://dx.doi.org/10.1371/journal.pone.0250326.

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With the growing utility of today’s conversational virtual assistants, the importance of user motivation in human–artificial intelligence interactions is becoming more obvious. However, previous studies in this and related fields, such as human–computer interaction, scarcely discussed intrinsic motivation (the motivation to interact with the assistants for fun). Previous studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation (the motivation to interact with the assistant for utilitarian purposes). The current study aims to cover intrinsic motivation by taking an affective engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments in which these two factors are manipulated by making participants believe they are interacting with the smart speaker “Amazon Echo” are conducted. Intrinsic motivation is measured both by using questionnaires and by covertly monitoring a five-minute free-choice period in the experimenter’s absence, during which the participants could decide for themselves whether to interact with the virtual assistants. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation. However, the results did not support our hypothesis that expectation and uncertainty have an interaction effect on intrinsic motivation. We then revised our hypothetical model of action selection accordingly and conducted a verification experiment of the effects of uncertainty. Results of the verification experiment showed that reducing uncertainty encourages more interactions and causes the motivation behind these interactions to shift from non-intrinsic to intrinsic.

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