Relevant bibliographies by topics / Agent conversationel

Academic literature on the topic 'Agent conversationel'

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Published: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Agent conversationel":

1

Qin, Zhen (Luther). "Conversational Breakdown Detector for a Motivational Interviewing Conversational Agent." IJournal: Student Journal of the Faculty of Information 9, no. 1 (December 19, 2023): 60–77. http://dx.doi.org/10.33137/ijournal.v9i1.42237.

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A conversational breakdown in human-chatbot interaction refers to a disruption or failure in the communicative flow between the human user and the chatbot. To recover a disrupted conversation, the first step is to detect the breakdown. Researchers have proposed methods using supervised learning and semi-supervised learning in dialogue systems to achieve the goal of detecting conversational breakdown. However, few studies have focused on detecting breakdowns in automated therapeutic conversations, especially conversations led by motivational interviewing chatbots. The presence of conversational breakdowns has negative impacts on the human-chatbot interaction, such as frustration, dissatisfaction, or loss of trust. This gap suggests a need to build a robust and efficient conversational breakdown detector that recognizes interruptions during the conversation. Conversational breakdown detection paves the way for further action to recover conversations. In this paper, I develop a novel, unifying framework called “CIMIC” for characterizing the conversational breakdowns of “MIBot,” a motivational interviewing conversational agent for smoking cessation. I collect 200 pieces of conversational data through Prolific and annotate them using the CIMIC framework with a group of four trained annotators. The annotated dataset is then applied as the training set to fine-tune GPT-3 models to build a conversational breakdown detector for the MIBot.
2

Watkinson, Neftali, Fedor Zaitsev, Aniket Shivam, Michael Demirev, Mike Heddes, Tony Givargis, Alexandru Nicolau, and Alexander Veidenbaum. "EdgeAvatar: An Edge Computing System for Building Virtual Beings." Electronics 10, no. 3 (January 20, 2021): 229. http://dx.doi.org/10.3390/electronics10030229.

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Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the length of the conversation without any specific targeted task. When a chatbot is embellished with an artistic approach that is meant to evoke an emotional response, then it is called a virtual being. On the other hand, conversational agents that interact with the physical world require the use of specialized hardware to sense and process captured information. In this article we describe EdgeAvatar, a system based on Edge Computing principles for the creation of virtual beings. The objective of the EdgeAvatar system is to provide a streamlined and modular framework for virtual being applications that are to be deployed in public settings. We also present two implementations that use EdgeAvatar and are inspired by historical figures to interact with visitors of the Venice Biennale 2019. EdgeAvatar can adapt to fit different approaches for AI powered conversations.
3

Belainine, Billal, Fatiha Sadat, and Hakim Lounis. "Modelling a Conversational Agent with Complex Emotional Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13710–11. http://dx.doi.org/10.1609/aaai.v34i10.7127.

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Chatbots or conversational agents have enjoyed great popularity in recent years. They surprisingly perform sensitive tasks in modern societies. However, despite the fact that they offer help, support, and fellowship, there is a task that is not yet mastered: dealing with complex emotions and simulating human sensations. This research aims to design an architecture for an emotional conversation agent for long-text conversations (multi-turns). This agent is intended to work in areas where the analysis of users feelings plays a leading role. This work refers to natural language understanding and response generation.
4

Pilato, Giovanni, Agnese Augello, and Salvatore Gaglio. "A Modular System Oriented to the Design of Versatile Knowledge Bases for Chatbots." ISRN Artificial Intelligence 2012 (March 5, 2012): 1–10. http://dx.doi.org/10.5402/2012/363840.

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The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate.
5

Zhong, Peixiang, Yong Liu, Hao Wang, and Chunyan Miao. "Keyword-Guided Neural Conversational Model." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14568–76. http://dx.doi.org/10.1609/aaai.v35i16.17712.

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We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.
6

Goh, Ong Sing, Chun Che Fung, Kok Wai Wong, and Arnold Depickere. "Embodied Conversational Agents for H5N1 Pandemic Crisis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 3 (March 20, 2007): 282–88. http://dx.doi.org/10.20965/jaciii.2007.p0282.

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This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place within the context of H5N1 pandemic crisis. A Crisis Communication Network, called CCNet, is established based on a novel algorithm incorporating natural language query and embodied conversation agent simultaneously. Another significant contribution of our work is the development of a Automated Knowledge Extraction Agent (AKEA) to capitalize on the tremendous amount of data that is now available online to support our experiments. What makes our system differs from typical conversational agents is the attempt to move away from strictly task-oriented dialogue.
7

Lin, Phoebe, Jessica Van Brummelen, Galit Lukin, Randi Williams, and Cynthia Breazeal. "Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13381–88. http://dx.doi.org/10.1609/aaai.v34i09.7061.

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Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.
8

Dhoolia, Pankaj, Vineet Kumar, Danish Contractor, and Sachindra Joshi. "Bootstrapping Dialog Models from Human to Human Conversation Logs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16024–25. http://dx.doi.org/10.1609/aaai.v35i18.18000.

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State-of-the-art commercial dialog platforms provide powerful tools to build a conversational agent. These platforms provide complete control to the dialog designer to model user-agent interactions. However, a dialog designer needs to rely on domain experts to manually build the dialog model -- by creating dialog flow nodes and modeling user intents. This process is laborious, time consuming and expensive and does not allow the designer to exploit human to human conversation logs effectively. In this work, we present a research prototype that can ingest human-to-human conversation logs between an end-user and an agent, and suggest user-intents and agent-responses, given a conversation context. We utilize human to human conversation logs to build two emulators: user and agent. An agent emulator models an agent response given the conversation context so far, and a user emulator outputs possible user responses. Our system is able to recommend conversational intents as well as conversation flow using emulators based on real-world data, thus making the process of designing a bot more efficient. To the best our knowledge this is the first system that enables data-driven dialog model creation by emulating users and agents.
9

Raj, Kanak, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, and Manas Gaur. "K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries." Proceedings of the AAAI Symposium Series 3, no. 1 (May 20, 2024): 219–26. http://dx.doi.org/10.1609/aaaiss.v3i1.31203.

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Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to appropriately tend to a user’s persona. This is crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response by supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. K-PERM achieves state-of-the- art performance on the popular FoCus dataset, containing real-world personalized conversations concerning global landmarks.We show that using responses from K-PERM can improve performance in state-of-the-art LLMs (GPT 3.5) by 10.5%, highlighting the impact of K-PERM for personalizing chatbots.
10

Wu, Zeqiu, Ryu Parish, Hao Cheng, Sewon Min, Prithviraj Ammanabrolu, Mari Ostendorf, and Hannaneh Hajishirzi. "InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions." Transactions of the Association for Computational Linguistics 11 (May 18, 2023): 453–68. http://dx.doi.org/10.1162/tacl_a_00559.

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Abstract In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either directly answers, asks for clarification, or provides relevant information to address user queries. The data supports two subtasks, evidence passage identification and response generation, as well as a human evaluation protocol to assess model performance. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering. Both systems significantly underperform humans, suggesting ample room for improvement in future studies.1
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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Agent conversationel":

1

Pistilli, Giada. "Pour une éthique de l'intelligence artificielle conversationnelle." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUL038.

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Cette recherche vise à sonder les complexités éthiques de l'intelligence artificielle (IA) conversationnelle, en se concentrant spécifiquement sur les grands modèles de langage et les agents conversationnels. Ce manuscrit construit un cadre qui allie l'analyse empirique au discours philosophique. Notre objectif est de plaider de toute urgence en faveur d'une structure éthique bien fondée pour l'IA conversationnelle, en soulignant la nécessité d'impliquer toutes les parties prenantes, des développeurs aux utilisateurs finaux. Tout d'abord, nous défendons l'intégration de l'ingénierie et d'autres disciplines scientifiques avec la philosophie, facilitant ainsi une compréhension plus nuancée des dimensions éthiques qui sous-tendent l'IA. Cette approche collaborative permet un discours éthique plus riche et mieux informé. Deuxièmement, nous préconisons l'utilisation dynamique de cadres éthiques appliqués en tant que guides fondamentaux pour la définition des objectifs initiaux d'un système d'IA. Ces cadres servent d'outils évolutifs qui s'adaptent aux complexités éthiques rencontrées au cours du développement et du déploiement. Enfin, sur la base d'une recherche pratique et interdisciplinaire, nous plaidons en faveur de la priorisation de l'IA étroite et spécifique à une tâche par rapport à l'intelligence artificielle générale, une position qui repose sur la faisabilité accrue de la surveillance éthique et de la contrôlabilité technique.Avec cette recherche, nous souhaitons contribuer à la littérature sur l'éthique de l'IA, en enrichissant le discours académique à la fois en philosophie et en informatique
This research aims to probe the ethical intricacies of conversational Artificial Intelligence (AI), specifically focusing on Large Language Models and conversational agents. This manuscript constructs a framework that melds empirical analysis with philosophical discourse. We aim to urgently advocate for a well-founded ethical structure for conversational AI, highlighting the necessity to involve all stakeholders, from developers to end-users. Firstly, we champion the integration of engineering and other scientific disciplines with philosophy, facilitating a more nuanced understanding of the ethical dimensions underpinning AI. This collaborative approach allows for a richer, more informed ethical discourse. Secondly, we advocate for the dynamic use of applied ethical frameworks as foundational guides for setting the initial objectives of an AI system. These frameworks serve as evolving tools that adapt to the ethical complexities encountered during development and deployment. Lastly, grounded in hands-on, interdisciplinary research, we make an argument for the prioritization of narrow, task-specific AI over Artificial General Intelligence, a stance that is based on the enhanced feasibility of ethical oversight and technical controllability.With this research, we aim to contribute to the literature on AI ethics, enriching the academic discourse in both philosophy and computer science
2

Ishii, Ryo. "Designing Conversational Interfaces for Facilitating Conversation using User's Gaze Behaviors." 京都大学 (Kyoto University), 2013. http://hdl.handle.net/2433/180472.

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VELMOVITSKY, PEDRO ELKIND. "IBOT: AN AGENT-BASED SOFTWARE FRAMEWORK FOR CREATING DOMAIN CONVERSATIONAL AGENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35430@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Chatbots são programas de computador que interagem com usuários utilizando linguagem natural. Desde sua origem, a tecnologia avançou significantemente e aplicações baseadas na nuvem de grandes empresas permitiram que desenvolvedores criassem chatbots inteligentes e eficientes. No entanto, não há muitas abordagens de desenvolvimento aos principais módulos de um chatbot que são flexíveis o suficiente para permitir a criação de chatbots diferentes para cada domínio, mantendo um robusto controle de diálogo na aplicação. Existem trabalhos que tentam desenvolver uma abordagem mais flexível, cada um com suas vantagens e desvantagens. Uma das vantagens mais notáveis é o uso de sistemas multiagentes para distribuir e realizar tarefas feitas por chatbots. Nesse contexto, este trabalho propõe um framework geral e flexível baseado em sistemas multiagentes para construir chatbots em um domínio escolhido pelo desenvolvedor, com controle de diálogo na aplicação. Esta solução usa uma adaptação da abordagem de estado da informação, e agentes de software, para gestão do diálogo. Para validar a arquitetura proposta, um cenário de uso com 4 chatbots de prova de conceito são analisados e discutidos.
Chatbots are computer programs that interact with users using natural language. Since its inception, the technology has advanced greatly and cloud-based platforms from big companies allow developers to create intelligent and efficient chatbots. However, there are not many development approaches to the main modules of a chatbot that are flexible enough to allow the creation of different chatbots for each domain, while maintaining a robust dialogue control in the application. There have been some works that try to develop a more flexible approach, each of them with their own advantages and disadvantages. One of the most notable advantages is the use of multi-agent systems to distribute and perform the tasks performed by the chatbot. In this context, this work proposes a general and flexible architecture based on multi-agent systems for building chatbots in any domain chosen by the developer, with dialogue control in the application. This architecture uses an adaptation of the information state approach, also using software agents, to perform dialogue management. To validate the proposed architecture, an user scenario involving the implementation of 4 proof of concept chatbots is analyzed discussed.
4

Jääskeläinen, Petra Pauliina. "Conversation Analysis as a Design Research Method for Designing Socioculturally Contextual Conversational Agents." Thesis, Uppsala universitet, Människa-datorinteraktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414120.

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This research paper presents a study exploring if using the Conversational Analysis (CA) method in design research could result in designing more socioculturally contextual conversational agents. The research specifically focused on understanding the 1) effect on the design outcome and 2) the role in the design process. This was studied through practice-based design research, participant evaluation of the design outcome, and expert interviews on the design method. The findings were analysed both qualitatively and quantitatively and showed, that socioculturally contextual design could potentially be a data-rich field of study with connections to design concepts such as inclusive design, affective design, design ethics, increased user experience, and further studies are therefore recommended. Furthermore, the study provided an understanding of the contexts in which the CA method may be useful in design, how it can potentially impact the design, and how to apply it to the design process and showed a positive effect on the design outcome in terms of socioculturally contextual design.
5

Hijjawi, Moh'd Hatim Husni. "ArabChat : an Arabic conversational agent." Thesis, Manchester Metropolitan University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547403.

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This thesis details the development of a novel and practical Conversational Agent for the Arabic language called ArabChat. A conversational Agent is a computer program that attempts to simulate conversations between machine and human. In this thesis, the term 'conversation' or 'utterance' refers to real-time chat exchange between machine and human. The proposed framework for developing the Arabic Conversational Agent (ArabChat) is based on Pattern Matching approach to handle users' conversations. The Pattern Matching approach is based on the matching process between a user's utterance and pre-scripted patterns that represents different topics organised through novel scripting structure. ArabChat classifies users' utterances as either question or nonquestion utterances in order to response to an utterance depending on its type (question or non-question). In addition, ArabChat has the ability to reply to an utterance targets many topics at the same time. Moreover, this thesis proposes to use the stemming technique (a process to return a word to its original root) as a pre-processing stage in ArabChat in order to convert the processed utterance's words to stemmed words and then match them with stemmed pre-scripted patterns. This proposal might decrease the number of needed patterns to script a domain to the minimum as discussed in this thesis. Furthermore, two new Arabic stemmers have been proposed, developed and discussed in this thesis In order to assess ArabChat, different techniques have been developed and used to validate its performance. Three experiments with online users have been carried out. The results have shown that ArabChat is effective.
6

Ly, Eric Thich Vi. "Chatter--a conversational telephone agent." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/29067.

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7

Rystedt, Beata, and Mia Zdybek. "Conversational agent as kitchen assistant." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230904.

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Chatbots, also called conversational agents, with speech interfaces are being used to a greater and greater extent, but there are still many areas that are not completely explored. The idea of this project was born out of the belief that there is a need for an assistant in the kitchen that is able to search for recipes, answer questions regarding them and guide and assist the user throughout the cooking process, all through conversation since the hands are busy. This paper begins with an introduction in the subject of conversational agents and the related technology, then similar, already existing studies and methods are presented with their pros and cons. After follows an in-depth explanation on how the program was constructed into a working kitchen assistant. Lastly, the users’ experiences of the performance and usability of the program was evaluated through tests and discussed. It turns out that conversational agents definitely can be integrated in the kitchen, and according to several sources, in a few years they will be implemented in all possible areas and change the technology of our time.
Konversationsrobotar med talgränssnitt används i allt större och större utsträckning men det finns fortfarande många områden som inte är helt utforskade. Idén till det här arbetet föddes ur uppfattningen att det existerar ett behov av en hjälpreda till köket som kan söka recept, svara på frågor kring receptet och vägleda och hjälpa användaren genom hela matlagningsprocessen i muntligt form eftersom händerna är upptagna med annat. Det här arbetet börjar med en introduktion i ämnet kring konversationsrobotar och tekniken bakom, sedan presenteras liknande arbeten och metoder som redan existerar inom området. Sedan följer en djupdykning i hur det framtagna programmet i detta arbete utvecklats fram till en fungerande matlagningsassisten. Till slut presenteras och diskuteras upplevelsen och användbarheten av konversationsroboten hos människor baserat på tester som gjorts. Det visar sig att konversationsrobotar mycket väl kan vara av användning i köket, och enligt flera källor kommer de att inom en snar framtid lavinartat implementeras i alla möjliga områden och förändra tekniken i vårt samhälle.
8

Jumaah, Ahmed Salih Fadhil. "Conversational Agent for Health Coaching." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3557/1/ahmed_jumaah_phd_thesis.pdf.

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Poor diet and physical inactivity are two of the biggest healthcare challenges we are facing, and are related to individuals lifestyle. In fact, a poor lifestyle is strongly correlated to chronic diseases, the leading causes for morbidity and mortality. Adhering to a healthy diet and following an active lifestyle are thus necessary to promote the overall health. However, maintaining a healthy diet and physically active lifestyle is hard. This is due to poor health literacy, lack of awareness, motivation and effective intervention support. Recent years have seen a blast of mHealth apps for health promotion, targeting in particular dietary behavior change. However, reviews showed difficulties in effective adoption and use of these applications in long-term health promotion. Contemporary approaches have focused on tracking user condition and few have analyzed aspects of user interaction with the system. To promote individuals health, users can benefit from some form of tailored guidance or coaching. That said, to ensure adequate users support, personalized care with a human agent in the loop can enhance the care delivery. Due to the increasing demand for continuous care by users and the shortage of caregiver resources, current health services are inefficient relative to user support and decreasing caregivers workload. Digital health devices can act as a key player in providing interactive health activities (via mobile and telemedicine systems), enhancing self-monitoring (through wearable tracker) and tailored coaching (using either automated or manual coaching systems). However, they’re ineffective in providing continuous health services and creating a balance between users support and caregivers workload. In addition, even with the technology existence, there is low motivation to maintain a healthy diet or exercise routines. Individuals use messaging applications as part of their regular daily routines. We harness the power of messaging chatbot systems to provide behavior change interventions for healthy lifestyle promotion. We particularly introduce the role of chatbot in task automation and adhering users to a health plan. Thus, in this thesis we present the concept of "Conversational User Interface in Health Coaching Interventions" that consists of a just-in-time health services to users and caregivers. We discuss ways to integrate the chatbot to assist caregivers with their tasks and support users with their condition. We get users to cue themselves to action by attaching the chatbot with users’ daily messaging routines. The service will eliminate the technology barrier and impairment for the users i.e., elderly. The chatbot accesses reliable user compliance data, sets adherence reminders by condition, and reports daily individuals adherence. The chatbot alerts the coach through a web application in critical cases. The approach facilitates adherence to health interventions by investigating a human-virtual agent mediated coaching approach on user motivation to adhere to the health promotion plan. The approach was validated with different experimentation phases. Using multiple research methods, this dissertation has made several contributions to the understanding of user motivation and the role of a semi-automated system with a human and virtual agent in tracking individuals with poor lifestyle. We will discuss the main contributions and experimentation results throughout the thesis.
9

Ravenet, Brian. "Modélisation de comportements non-verbaux et d'attitudes sociales dans la simulation de groupes conversationnels." Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0075/document.

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Les Agents Conversationnels Animés sont des personnages virtuels dont la fonction principale est d'interagir avec l'utilisateur. Ils sont utilisés dans différents domaines tels que l'assistance personnelle, l'entrainement social ou les jeux vidéo et afin d'améliorer leur potentiel, il est possible de leur donner la capacité d'exprimer des comportements similaires à ceux des humains. Les utilisateurs, conscient d'interagir avec une machine, sont tout de même capable d'analyser et d'identifier des comportements sociaux à travers les signaux émis par les agents. La recherche en ACA s'est longtemps intéressée aux mécanismes de reproduction et de reconnaissance des émotions au sein de ces personnages virtuels et maintenant l'intérêt se porte sur la capacité d'exprimer différentes attitudes sociales. Ces attitudes reflètent un style comportemental et s'expriment à travers différentes modalités du corps comme les expressions faciales, les regards ou les gestes par exemple. Nous avons proposé un modèle permettant à un agent de produire différents comportements non-verbaux traduisant l'expression d'attitudes sociales dans une conversation. L'ensemble des comportements générés par notre modèle permettent à un groupe d'agents animés par celui-ci de simuler une conversation, sans tenir compte du contenu verbal. Deux évaluations du modèle ont été conduites, l'une sur Internet et l'autre dans un environnement de réalité virtuelle, afin de vérifier que les attitudes étaient bien reconnues
Embodied Conversational Agents are virtual characters which main purpose is to interact with a human user. They are used in various domains such as personal assistance, social training or video games for instance. In order to improve their capabilities, it is possible to give them the ability to produce human-like behaviors. The users, even if they are aware that they interact with a machine, are still capable of analyzing and identifying social behaviors through the signals produced by these virtual characters. The research in Embodied Conversational Agents has focused for a long time on the reproduction and recognition of emotions by virtual characters and now the focus is on the ability to express different social attitudes. These attitudes show a behavioral style and are expressed through different modalities of the body, like the facial expressions, the gestures or the gazes for instance. We proposed a model that allows an agent to produce different nonverbal behaviors expressing different social attitudes in a conversation. The whole set of behaviors produced by our model allows a goup of agents animated by it to simulate a conversation, without any verbal content. Two evaluations of the model were conducted, one on the Internet and one in a Virtual Reality environment, to verify that the attitudes produced are well recognized
10

O'Shea, Karen Suzanne. "A semantic-based conversational agent framework." Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543751.

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Books on the topic "Agent conversationel":

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Long, Rob. Conversations with my agent. New York: Dutton, 1997.

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Long, Rob. Conversations with my agent. London: Faber and Faber, 1996.

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Perez-Marin, Diana. Conversational agents and natural language interaction: Techniques and effective practices. Hershey, PA: Information Science Reference, 2011.

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Broth, Matthias. Agents secrets: Le public dans la construction interactive de la représentation théâtrale. Uppsala, Sweden: Uppsala Universitet, 2002.

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Stivers, Tanya. Prescribing under pressure: Patient-physician conversations and antibiotics. New York, NY: Oxford University Press, 2007.

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Hamilton, Dan. Perfect phrases for real estate agents & brokers. New York: McGraw-Hill, 2009.

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McKenna, Michael. Power, Social Inequities, and the Conversational Theory of Moral Responsibility. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190609610.003.0002.

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According to the conversational theory, moral responsibility is essentially interpersonal and communicative. Indeed, it is not only communicative; it has a conversational dimension. On the conversational theory, an agent’s actions—those that are candidates for blameworthiness or praiseworthiness—are potential bearers of meaning, where meaning is a function of the quality of an agent’s will. This meaning is analogous to the meaning a competent speaker conveys when she engages in conversation. Call this “agent meaning.” Like speaker meaning, agent meaning can be affected by the interpretive framework whereby others interpret the meaning of an agent’s actions. One aspect of the conversational theory that remains unexplored is how asymmetrical power-dynamics, especially resulting from social inequities, shape the interpretive framework that in turn influences the context in which morally responsible agents act. This chapter explores this topic and thereby exposes an unpalatable side to the nature of our moral responsibility practices.
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Long, Rob. Conversations avec mon agent. Actes Sud, 1999.

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Long, Rob. Conversations with My Agent. Penguin Publishing Group, 2009.

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Cassell, Justine, Joseph Sullivan, Scott Prevost, and Elizabeth F. Churchill, eds. Embodied Conversational Agents. The MIT Press, 2000. http://dx.doi.org/10.7551/mitpress/2697.001.0001.

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Book chapters on the topic "Agent conversationel":

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Dolamic, Ljiljana. "Conversational Agents." In Large Language Models in Cybersecurity, 45–53. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_4.

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AbstractConversational agents (CA) are engaged in interactive conversations with users, providing responses and assistance while combining Natural Language Processing (NLP), Understanding (NLU), and Generating (NLG) techniques. Two tiers of conversational agent derivation from Large Language Models (LLMs) exist. The first tier involves conversational fine-tuning from datasets, representing expected user questions and desired conversational agent responses. The second tier requires manual prompting by human operators and evaluation of model output, which is then used for further fine-tuning. Fine-tuning with Reinforcement Learning from Human Feedback (RLHF) models perform better but are resource-intensive and specific for each model. Another critical difference in the performance of various CA is their ability to access auxiliary services for task delegation.
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Martin, Francisco J., Enric Plaza, and Juan A. Rodríguez-Aguilar. "Conversation Protocols: Modeling and Implementing Conversations in Agent-Based Systems." In Issues in Agent Communication, 249–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10722777_17.

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Phillips, Laurence R., and Hamilton E. Link. "The Role of Conversation Policy in Carrying Out Agent Conversations." In Issues in Agent Communication, 132–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10722777_9.

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Oudeyer, Pierre-Yves, and Jean-Luc Koning. "Modeling Soccer-Robots Strategies through Conversation Policies." In Agent Systems, Mobile Agents, and Applications, 249–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-45347-5_20.

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Cost, R. Scott, Yannis Labrou, and Tim Finin. "Coordinating Agents using Agent Communication Languages Conversations." In Coordination of Internet Agents, 183–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04401-8_7.

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Rossen, Brent, Scott Lind, and Benjamin Lok. "Human-Centered Distributed Conversational Modeling: Efficient Modeling of Robust Virtual Human Conversations." In Intelligent Virtual Agents, 474–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04380-2_52.

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Barbuceanu, Mihai, and Wai-Kau Lo. "Integrating Conversational Interaction and Constraint Based Reasoning in an Agent Building Shell." In Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems, 144–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47772-1_13.

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Kimura, Mikako, and Yasuhiko Kitamura. "Embodied Conversational Agent Based on Semantic Web." In Agent Computing and Multi-Agent Systems, 734–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802372_87.

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Huang, Hung-Hsuan. "Embodied Conversational Agents." In The Wiley Handbook of Human Computer Interaction, 599–614. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118976005.ch26.

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Suendermann-Oeft, David. "Modern Conversational Agents." In Technologien für digitale Innovationen, 63–84. Wiesbaden: Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-04745-0_4.

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Conference papers on the topic "Agent conversationel":

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Sharma, Ashish, Inna W. Lin, Adam S. Miner, Dave C. Atkins, and Tim Althoff. "Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/747.

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Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden. Psychologists have repeatedly demonstrated that empathy, the ability to understand and feel the emotions and experiences of others, is a key component leading to positive outcomes in supportive conversations. However, recent studies have shown that highly empathic conversations are rare in online mental health platforms. In this paper, we work towards improving empathy in online mental health support conversations. We introduce a new task of empathic rewriting which aims to transform low-empathy conversational posts to higher empathy. Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context. Here we propose Partner, a deep reinforcement learning (RL) agent that learns to make sentence-level edits to posts in order to increase the expressed level of empathy while maintaining conversation quality. Our RL agent leverages a policy network, based on a transformer language model adapted from GPT-2, which performs the dual task of generating candidate empathic sentences and adding those sentences at appropriate positions. Through a combination of automatic and human evaluation, we demonstrate that Partner successfully generates more empathic, specific, and diverse responses and outperforms NLP methods from related tasks such as style transfer and empathic dialogue generation.
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Kurylek, Bartosz, Arthur Camara, Akash Nandi, and Evangelos Markopoulos. "A Novel Agent-Based Framework for Conversational Data Analysis and Personal AI Systems." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004649.

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This paper introduces a novel agent-based framework that leverages conversational data to enhance Large Language Models (LLMs) with personalized knowledge, enabling the creation of Artificial Personal Intelligence (API) systems. The proposed framework addresses the challenge of collecting and analysing unstructured conversational data by utilizing LLM agents and embeddings to efficiently process, organize, and extract insights from conversations. The system architecture integrates knowledge data aggregation and agent-based conversational data extraction. The knowledge data aggregation method employs LLMs and embeddings to create a dynamic, multi-level hierarchy for organizing information based on conceptual similarity and topical relevance. The agent-based component utilizes an LLM Agent to handle user queries, extracting relevant information and generating specialized theme datasets for comprehensive analysis. The framework's effectiveness is demonstrated through empirical analysis of real-world conversational data and a user survey. However, limitations such as the need for further testing of scalability and performance under large-scale, real-world conditions and potential biases introduced by LLMs are acknowledged. Future research should focus on extensive real-world testing and the integration of additional conversational qualities to further enhance the framework's capabilities, ultimately enabling more personalized and context-aware AI assistance.
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Ismail, Jabri, Aboulbichr Ahmed, and El ouaazizi Aziza. "Improving a Sequence-to-sequence NLP Model using a Reinforcement Learning Policy Algorithm." In 12th International Conference on Artificial Intelligence, Soft Computing and Applications. Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122317.

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Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue’s future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.
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Divekar, Rahul R., Xiangyang Mou, Lisha Chen, Maíra Gatti de Bayser, Melina Alberio Guerra, and Hui Su. "Embodied Conversational AI Agents in a Multi-modal Multi-agent Competitive Dialogue." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/940.

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In a setting where two AI agents embodied as animated humanoid avatars are engaged in a conversation with one human and each other, we see two challenges. One, determination by the AI agents about which one of them is being addressed. Two, determination by the AI agents if they may/could/should speak at the end of a turn. In this work we bring these two challenges together and explore the participation of AI agents in multi-party conversations. Particularly, we show two embodied AI shopkeeper agents who sell similar items aiming to get the business of a user by competing with each other on the price. In this scenario, we solve the first challenge by using headpose (estimated by deep learning techniques) to determine who the user is talking to. For the second challenge we use deontic logic to model rules of a negotiation conversation.
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Sanders, Abraham, Mara Schwartz, Albert Chang, Shannon Briggs, Jonas Braasch, Dakuo Wang, Mei Si, and Tomek Strzalkowski. "Towards a Proper Evaluation of Automated Conversational Systems." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003276.

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Efficient evaluation of dialogue agents is a major problem in conversational AI, with current research still relying largely on human studies for method validation. Recently, there has been a trend toward the use of automatic self-play and bot-bot evaluation as an approximation for human ratings of conversational systems. Such methods promise to alleviate the time and financial costs associated with human evaluation, and current proposed methods show moderate to strong correlation with human judgements. In this study, we further investigate the fitness of end-to-end self-play and bot-bot interaction for dialogue system evaluation. Specifically, we perform a human study to confirm self-play evaluations of a recently proposed agent that implements a GPT-2 based response generator on the Persuasion For Good charity solicitation task. This agent leverages Progression Function (PF) models to predict the evolving acceptability of an ongoing dialogue and uses dialogue rollouts to proactively simulate how candidate responses may impact the future success of the conversation. The agent was evaluated in an automatic self-play setting, using automatic metrics to estimate sentiment and intent to donate in each simulated dialogue. This evaluation indicated that sentiment and intent to donate were higher (p < 0.05) across dialogues involving the progression-aware agents with rollouts, compared to a baseline agent with no rollout-based planning mechanism. To validate the use of self-play in this setting, we follow up by conducting a human evaluation of this same agent on a range of factors including convincingness, aggression, competence, confidence, friendliness, and task utility on the same Persuasion For Good solicitation task. Results show that human users agree with previously reported automatic self-play results with respect to agent sentiment, specifically showing improvement in friendliness and confidence in the experimental condition; however, we also discover that for the same agent, humans reported a lower desire to use it in the future compared to the baseline. We perform a qualitative sentiment analysis of participant feedback to explore possible reasons for this, and discuss implications for self-play and bot-bot interaction as a general framework for evaluating conversational systems.
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Sun, Xin, Emiel Krahmer, Jan De Wit, Reinout Wiers, and Jos A. Bosch. "Plug and Play Conversations: The Micro-Conversation Scheme for Modular Development of Hybrid Conversational Agent." In CSCW '23: Computer Supported Cooperative Work and Social Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3584931.3606998.

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Monteiro, Mateus de Souza, Vinícius Carvalho Pereira, and Luciana Cardoso de Castro Salgado. "Design conversacional de chatbots: cultura, linguagem e participação." In Anais Estendidos do Simpósio Brasileiro de Sistemas Colaborativos. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbsc_estendido.2023.25653.

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The challenges in conversational design of chatbots that consider domain and/or user cultural issues are diverse. Among them, the lack of conceptual and/or theoretical approaches to guide the designer during the conversational design process. As a result, designers often rely on their own assumptions and linguistic preferences in conversation design. In this work, we discuss how the areas of HCI and CSCW can contribute to the inclusion of cultural issues in the participatory design process of conversational agents.
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Chatterjee, Ajay, and Shubhashis Sengupta. "Intent Mining from past conversations for Conversational Agent." In Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.366.

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Chatterjee, Ajay, and Shubhashis Sengupta. "Intent Mining from past conversations for Conversational Agent." In Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.366.

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Matos, Vinícius Bitencourt, Ricardo Grava, Rodrigo Tavares, Marcos Menon José, Paulo Pirozelli, Anarosa A. F. Brandão, Sarajane M. Peres, and Fábio G. Cozman. "Coordination within Conversational Agents with Multiple Sources." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234533.

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Conversational agents can now operate with language models, rules, ontologies and varied other sources to provide smooth dialogue. However, the coordination of multiple sources in conversational agents is a challenge. We present a mechanism to effectively orchestrate multiple sources in a conversational agent, by relying on a client-server approach with an associated prompt generation module that deals with heterogeneous domain-oriented modules. As a detailed use case, we describe the architecture of a chatbot specialised in topics related to the Brazilian coast, and we study the benefits of our approach.

Reports on the topic "Agent conversationel":

1

Murray, William R., Michelle Sams, William H. DeSmedt, and Donald Loritz. Mentor: Dialog Agent System for Mentoring and Conversational Role-Playing. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada395194.

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Johnson, W. L. Dramatic Expression in Opera, and Its Implications for Conversational Agents. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada462170.

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